Paper Digest: Recent Papers on Question Answering
Paper Digest Team extracted all recent Question Answering related papers on our radar, and generated highlight sentences for them. The results are then sorted by relevance & date. In addition to this ‘static’ page, we also provide a real-time version of this article, which has more coverage and is updated in real time to include the most recent updates on this topic.
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TABLE 1: Paper Digest: Recent Papers on Question Answering
| Paper | Author(s) | Source | Date | |
|---|---|---|---|---|
| 1 | A Dataset for Spatiotemporal-Sensitive POI Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing Question-Answering (QA) datasets lack sufficient spatiotemporal-sensitive questions, making them inadequate benchmarks for evaluating models’ spatiotemporal reasoning capabilities. To address this gap, we introduce POI-QA, a novel spatiotemporal-sensitive QA dataset centered on Point of Interest (POI), constructed through three key steps: mining and aligning open-source vehicle trajectory data from GAIA with high-precision geographic POI data, rigorous manual validation of noisy spatiotemporal facts, and generating bilingual (Chinese/English) QA pairs that reflect human-understandable spatiotemporal reasoning tasks. |
XIAO HAN et. al. | arxiv-cs.LG | 2025-05-16 |
| 2 | Scaling Reasoning Can Improve Factuality in Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we thoroughly examine LLM reasoning within complex open-domain question-answering (QA) scenarios. |
Mike Zhang; Johannes Bjerva; Russa Biswas; | arxiv-cs.CL | 2025-05-16 |
| 3 | HumaniBench: A Human-Centric Framework for Large Multimodal Models Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce HumaniBench, a holistic benchmark of 32K real-world image question pairs, annotated via a scalable GPT4o assisted pipeline and exhaustively verified by domain experts. |
SHAINA RAZA et. al. | arxiv-cs.CV | 2025-05-16 |
| 4 | Semantic Caching of Contextual Summaries for Efficient Question-Answering with Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a novel semantic caching approach for storing and reusing intermediate contextual summaries, enabling efficient information reuse across similar queries in LLM-based QA workflows. |
Camille Couturier; Spyros Mastorakis; Haiying Shen; Saravan Rajmohan; Victor Rühle; | arxiv-cs.CL | 2025-05-16 |
| 5 | CAFE: Retrieval Head-based Coarse-to-Fine Information Seeking to Enhance Multi-Document QA Capability Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, they still face challenges in balancing retrieval precision and recall, impacting their efficacy in answering questions. To address this, we introduce $\textbf{CAFE}$, a two-stage coarse-to-fine method to enhance multi-document question-answering capacities. |
Han Peng; Jinhao Jiang; Zican Dong; Wayne Xin Zhao; Lei Fang; | arxiv-cs.CL | 2025-05-15 |
| 6 | Enhancing Multi-Image Question Answering Via Submodular Subset Selection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose an enhancement for retriever framework introduced in MIRAGE model using submodular subset selection techniques. |
Aaryan Sharma; Shivansh Gupta; Samar Agarwal; Vishak Prasad C.; Ganesh Ramakrishnan; | arxiv-cs.CV | 2025-05-15 |
| 7 | What Does Neuro Mean to Cardio? Investigating The Role of Clinical Specialty Data in Medical LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce S-MedQA, an English medical question-answering (QA) dataset for benchmarking large language models in fine-grained clinical specialties. |
Xinlan Yan; Di Wu; Yibin Lei; Christof Monz; Iacer Calixto; | arxiv-cs.CL | 2025-05-15 |
| 8 | PT-MoE: An Efficient Finetuning Framework for Integrating Mixture-of-Experts Into Prompt Tuning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Parameter-efficient fine-tuning (PEFT) methods have shown promise in adapting large language models, yet existing approaches exhibit counter-intuitive phenomena: integrating router into prompt tuning (PT) increases training efficiency yet does not improve performance universally; parameter reduction through matrix decomposition can improve performance in specific domains. Motivated by these observations and the modular nature of PT, we propose PT-MoE, a novel framework that integrates matrix decomposition with mixture-of-experts (MoE) routing for efficient PT. |
Zongqian Li; Yixuan Su; Nigel Collier; | arxiv-cs.CL | 2025-05-14 |
| 9 | WixQA: A Multi-Dataset Benchmark for Enterprise Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Critically, evaluating end-to-end RAG systems requires benchmarks comprising not only question–answer pairs but also the specific knowledge base (KB) snapshot from which answers were derived. To address this need, we introduce WixQA, a benchmark suite featuring QA datasets precisely grounded in the released KB corpus, enabling holistic evaluation of retrieval and generation components. |
DVIR COHEN et. al. | arxiv-cs.AI | 2025-05-13 |
| 10 | Efficient and Reproducible Biomedical Question Answering Using Retrieval Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study systematically examines a Retrieval-Augmented Generation (RAG) system for biomedical QA, evaluating retrieval strategies and response time trade-offs. |
Linus Stuhlmann; Michael Alexander Saxer; Jonathan Fürst; | arxiv-cs.IR | 2025-05-12 |
| 11 | DocVXQA: Context-Aware Visual Explanations for Document Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose DocVXQA, a novel framework for visually self-explainable document question answering. |
MOHAMED ALI SOUIBGUI et. al. | arxiv-cs.CV | 2025-05-12 |
| 12 | Multi-Domain Audio Question Answering Toward Acoustic Content Reasoning in The DCASE 2025 Challenge Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present Task 5 of the DCASE 2025 Challenge: an Audio Question Answering (AQA) benchmark spanning multiple domains of sound understanding. |
CHAO-HAN HUCK YANG et. al. | arxiv-cs.SD | 2025-05-12 |
| 13 | Visually Interpretable Subtask Reasoning for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recent work improves interpretability in multimodal large language models (MLLMs) by decomposing tasks into sub-task programs, but these methods are computationally expensive and less accurate due to poor adaptation to target data. To address this, we introduce VISTAR (Visually Interpretable Subtask-Aware Reasoning Model), a subtask-driven training framework that enhances both interpretability and reasoning by generating textual and visual explanations within MLLMs. |
Yu Cheng; Arushi Goel; Hakan Bilen; | arxiv-cs.CV | 2025-05-12 |
| 14 | NeoQA: Evidence-based Question Answering with Generated News Events Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Questions initially requiring retrieval may become answerable from pretraining knowledge as newer models incorporate more recent information during pretraining, making it difficult to distinguish evidence-based reasoning from recall. We introduce NeoQA (News Events for Out-of-training Question Answering), a benchmark designed to address this issue. |
Max Glockner; Xiang Jiang; Leonardo F. R. Ribeiro; Iryna Gurevych; Markus Dreyer; | arxiv-cs.CL | 2025-05-09 |
| 15 | Fine-Tuning Large Language Models and Evaluating Retrieval Methods for Improved Question Answering on Building Codes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study focuses on identifying a suitable retriever method for building codes and optimizing the generational capability of the language model using fine-tuning techniques. |
Mohammad Aqib; Mohd Hamza; Qipei Mei; Ying Hei Chui; | arxiv-cs.CL | 2025-05-07 |
| 16 | IndicSQuAD: A Comprehensive Multilingual Question Answering Dataset for Indic Languages Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present IndicSQuAD, a comprehensive multi-lingual extractive QA dataset covering nine major Indic languages, systematically derived from the SQuAD dataset. |
Sharvi Endait; Ruturaj Ghatage; Aditya Kulkarni; Rajlaxmi Patil; Raviraj Joshi; | arxiv-cs.CL | 2025-05-06 |
| 17 | A Reasoning-Focused Legal Retrieval Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: An obstacle to the development of specialized RAG systems is the lack of realistic legal RAG benchmarks which capture the complexity of both legal retrieval and downstream legal question-answering. To address this, we introduce two novel legal RAG benchmarks: Bar Exam QA and Housing Statute QA. |
LUCIA ZHENG et. al. | arxiv-cs.CL | 2025-05-06 |
| 18 | Structure Causal Models and LLMs Integration in Medical Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a causal inference framework for the MedVQA task, which effectively eliminates the relative confounding effect between the image and the question to ensure the precision of the question-answering (QA) session. |
Zibo Xu; Qiang Li; Weizhi Nie; Weijie Wang; Anan Liu; | arxiv-cs.CV | 2025-05-05 |
| 19 | ScreenQA: Large-Scale Question-Answer Pairs Over Mobile App Screenshots Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce ScreenQA, a novel benchmarking dataset designed to advance screen content understanding through question answering. |
YU-CHUNG HSIAO et. al. | naacl | 2025-05-04 |
| 20 | Automatic Evaluation of Healthcare LLMs Beyond Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work is focused on the healthcare domain, where both factuality and discourse matter greatly. It introduces a comprehensive, multi-axis suite for healthcare LLM evaluation, exploring correlations between open and close benchmarks and metrics. |
ANNA ARIAS-DUART et. al. | naacl | 2025-05-04 |
| 21 | Hybrid Graphs for Table-and-Text Based Question Answering Using LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a novel Hybrid Graph-based approach for Table-Text QA that leverages LLMs without fine-tuning. |
Ankush Agarwal; Chaitanya Devaguptapu; Ganesh S; | naacl | 2025-05-04 |
| 22 | Analyzing and Improving Coherence of Large Language Models in Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we analyze the behavior of multiple LLMs, including Mixtral-8x7B, Llama2-70b, Smaug-72b, and Phi-3, when dealing with multiple lexical variations of the same info-seeking questions. |
Ivano Lauriola; Stefano Campese; Alessandro Moschitti; | naacl | 2025-05-04 |
| 23 | Diversify-verify-adapt: Efficient and Robust Retrieval-Augmented Ambiguous Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although the iterative RAG approach has been proposed to address this problem, it comes at the cost of significantly reduced efficiency. To address these issues, we propose the diversify-verify-adapt (DIVA) framework. |
YEONJUN IN et. al. | naacl | 2025-05-04 |
| 24 | PeerQA: A Scientific Question Answering Dataset from Peer Reviews Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present PeerQA, a real-world, scientific, document-level Question Answering (QA) dataset. |
Tim Baumgärtner; Ted Briscoe; Iryna Gurevych; | naacl | 2025-05-04 |
| 25 | Bottom-Up Synthesis of Knowledge-Grounded Task-Oriented Dialogues with Iteratively Self-Refined Prompts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a bottom-up conversation synthesis approach, where QA pairs are generated first and then combined into a coherent dialogue. |
Kun Qian; Maximillian Chen; Siyan Li; Arpit Sharma; Zhou Yu; | naacl | 2025-05-04 |
| 26 | RTV-Bench: Benchmarking MLLM Continuous Perception, Understanding and Reasoning Through Real-Time Video Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, current benchmarks inadequately evaluate their ability to perform these tasks continuously in dynamic, real-world environments. To bridge this gap, we introduce RTV-Bench, a fine-grained benchmark for MLLM real-time video analysis. |
SHUHANG XUN et. al. | arxiv-cs.CV | 2025-05-04 |
| 27 | MAPWise: Evaluating Vision-Language Models for Advanced Map Queries Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study investigates the efficacy of VLMs in answering questions based on choropleth maps, which are widely used for data analysis and representation. To facilitate and encourage research in this area, we introduce a novel map-based question-answering benchmark, consisting of maps from three geographical regions (United States, India, China), each containing around 1000 questions. |
SRIJA MUKHOPADHYAY et. al. | naacl | 2025-05-04 |
| 28 | Pointwise Mutual Information As A Performance Gauge for Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, there is no method to date that exploits this phenomenon to improve generation. To fill this gap, in this study, we show that the pointwise mutual information between a context and a question is an effective gauge for language model performance. |
TIANYU LIU et. al. | naacl | 2025-05-04 |
| 29 | CoRAC: Integrating Selective API Document Retrieval with Question Semantic Intent for Code Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a knowledge-based framework, CoRAC, an automatic code question responder that enhances understanding through selective API document retrieval and question semantic intent clustering. |
YunSeok Choi; CheolWon Na; Jee-Hyong Lee; | naacl | 2025-05-04 |
| 30 | CuriousLLM: Elevating Multi-Document Question Answering with LLM-Enhanced Knowledge Graph Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose CuriousLLM, an enhancement that integrates a curiosity-driven reasoning mechanism into an LLM agent. |
Zukang Yang; Zixuan Zhu; Jennifer Zhu; | naacl | 2025-05-04 |
| 31 | SUNAR: Semantic Uncertainty Based Neighborhood Aware Retrieval for Complex QA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce SUNAR, a novel approach that leverages LLMs to guide a Neighborhood Aware Retrieval process. |
Venktesh V; Mandeep Rathee; Avishek Anand; | naacl | 2025-05-04 |
| 32 | AdaCAD: Adaptively Decoding to Balance Conflicts Between Contextual and Parametric Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a fine-grained, instance-level approach called AdaCAD, which dynamically infers the weight of adjustment based on the degree of conflict, as measured by the Jensen-Shannon divergence between distributions representing contextual and parametric knowledge. |
Han Wang; Archiki Prasad; Elias Stengel-Eskin; Mohit Bansal; | naacl | 2025-05-04 |
| 33 | VoiceTextBlender: Augmenting Large Language Models with Speech Capabilities Via Single-Stage Joint Speech-Text Supervised Fine-Tuning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Another critical challenge with SpeechLMs is catastrophic forgetting, where models optimized for speech tasks suffer significant degradation in text-only performance. To mitigate these issues, we propose a novel single-stage joint speech-text SFT approach on the low-rank adaptation (LoRA) of the LLM backbone. |
YIFAN PENG et. al. | naacl | 2025-05-04 |
| 34 | K-COMP: Retrieval-Augmented Medical Domain Question Answering With Knowledge-Injected Compressor Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View |
Jeonghun Cho; Gary Lee; | naacl | 2025-05-04 |
| 35 | Benchmarking Large Language Models on Answering and Explaining Challenging Medical Questions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Moreover, the lack of reference explanations means we cannot easily evaluate the reasoning of model decisions, a crucial component of supporting doctors in making complex medical decisions. To address these challenges, we construct two new datasets: JAMA Clinical Challenge and Medbullets. |
Hanjie Chen; Zhouxiang Fang; Yash Singla; Mark Dredze; | naacl | 2025-05-04 |
| 36 | H-STAR: LLM-driven Hybrid SQL-Text Adaptive Reasoning on Tables Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing methods employ either textual reasoning, which excels in semantic interpretation but struggles with mathematical operations, or symbolic reasoning, which handles computations well but lacks semantic understanding. This paper introduces a novel algorithm H-STAR that integrates both symbolic and semantic (textual) approaches in a two-stage process to address these limitations. |
Nikhil Abhyankar; Vivek Gupta; Dan Roth; Chandan K. Reddy; | naacl | 2025-05-04 |
| 37 | Reverse Question Answering: Can An LLM Write A Question So Hard (or Bad) That It Can’t Answer? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: By finding question and answer types that lead to RQA errors, we suggest improvements for LLM reasoning. |
NISHANT BALEPUR et. al. | naacl | 2025-05-04 |
| 38 | SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, adapting general-purpose RAG systems to specialized fields such as science and medicine poses unique challenges due to distribution shifts and limited access to domain-specific data. To tackle this, we propose SimRAG, a self-training approach that equips LLMs with joint capabilities of question answering and question generation for domain adaptation. |
RAN XU et. al. | naacl | 2025-05-04 |
| 39 | DeCAP: Context-Adaptive Prompt Generation for Debiasing Zero-shot Question Answering in Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing zero-shot methods are efficient but failto consider context and prevent bias propagation in the answers. To address this, we propose *DeCAP*, a method for debiasing LLMs usingContext-Adaptive Prompt Generation. |
Suyoung Bae; YunSeok Choi; Jee-Hyong Lee; | naacl | 2025-05-04 |
| 40 | From Generating Answers to Building Explanations: Integrating Multi-Round RAG and Causal Modeling for Scientific QA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by findings from the social sciences, we present an implemented causal QA approach that combines iterative RAG with guidance from a formal model of causation. |
VICTOR BARRES et. al. | naacl | 2025-05-04 |
| 41 | Direct Preference Optimization of Video Large Multimodal Models from Language Model Reward Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While previous studies have explored using large multimodal models (LMMs) as reward models for guiding preference modeling, their ability to accurately assess the quality of generated responses and their alignment with video content has not been conclusively demonstrated. This paper introduces a novel framework that utilizes detailed video captions as a proxy of video content, enabling language models to incorporate this information as supporting evidence for scoring video Question Answering (QA) predictions. |
RUOHONG ZHANG et. al. | naacl | 2025-05-04 |
| 42 | THREAD: Thinking Deeper with Recursive Spawning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Large language models (LLMs) have shown impressive capabilities across diverse settings, but still struggle as the length and complexity of the context increases. To address this challenge, we propose Thinking Recursively and Dynamically (ThReaD). |
Philip Schroeder; Nathaniel W. Morgan; Hongyin Luo; James R. Glass; | naacl | 2025-05-04 |
| 43 | MultiChartQA: Benchmarking Vision-Language Models on Multi-Chart Problems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Current benchmarks primarily focus on single-chart tasks, neglecting the multi-hop reasoning required to extract and integrate information from multiple charts, which is essential in practical applications. To fill this gap, we introduce MultiChartQA, a benchmark that evaluates MLLMs’ capabilities in four key areas: direct question answering, parallel question answering, comparative reasoning, and sequential reasoning. |
Zifeng Zhu; Mengzhao Jia; Zhihan Zhang; Lang Li; Meng Jiang; | naacl | 2025-05-04 |
| 44 | ProMQA: Question Answering Dataset for Multimodal Procedural Activity Understanding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present a novel evaluation dataset, ProMQA, to measure the advancement of systems in application-oriented scenarios. |
KIMIHIRO HASEGAWA et. al. | naacl | 2025-05-04 |
| 45 | MoEMoE: Question Guided Dense and Scalable Sparse Mixture-of-Expert for Multi-source Multi-modal Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we formulate a novel question-answer generation (QAG) framework in an environment containing multi-source, multimodal information. |
Vinay Kumar Verma; Shreyas Sunil Kulkarni; Happy Mittal; Deepak Gupta; | naacl | 2025-05-04 |
| 46 | Generating Complex Question Decompositions in The Face of Distribution Shifts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: One way of improving LLM training and fine-tuning is to leverage synthetic training data, but the superior performance of supervised approaches collapses in the face of distribution shifts, making them unsuitable for generating synthetic data across new domains and at scale. To address this, we propose an approach to generate synthetic decomposition data with only five annotated examples; we do this by (i) extending recent advancements in using LLM-as-judge and for reranking in novel ways, as well as (ii) using a panel of smaller-sized LLMs for data generation instead of resource-intensive larger models. |
Kelvin Han; Claire Gardent; | naacl | 2025-05-04 |
| 47 | VisDoM: Multi-Document QA with Visually Rich Elements Using Multimodal Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose VisDoMRAG, a novel multimodal Retrieval Augmented Generation (RAG) approach that simultaneously utilizes visual and textual RAG, combining robust visual retrieval capabilities with sophisticated linguistic reasoning. |
MANAN SURI et. al. | naacl | 2025-05-04 |
| 48 | Elevating Legal LLM Responses: Harnessing Trainable Logical Structures and Semantic Knowledge with Legal Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose the Logical-Semantic Integration Model (LSIM), a novel supervised framework that bridges semantic and logical coherence. |
RUJING YAO et. al. | naacl | 2025-05-04 |
| 49 | Interaction Configurations and Prompt Guidance in Conversational AI for Question Answering in Human-AI Teams Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Extending from our initial formative study, which revealed challenges in human utilization of conversational AI support, we designed two configurations for prompt guidance: a Nudging approach, where the AI suggests potential responses for human agents, and a Highlight strategy, emphasizing crucial parts of reference documents to aid human responses. |
JAEYOON SONG et. al. | arxiv-cs.HC | 2025-05-02 |
| 50 | TRAVELER: A Benchmark for Evaluating Temporal Reasoning Across Vague, Implicit and Explicit References Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although existing benchmarks address a system’s ability to reason about and resolve temporal references, systematic evaluation of specific temporal references remains limited. Towards closing this gap, we introduce TRAVELER, a novel synthetic benchmark dataset that follows a Question Answering paradigm and consists of questions involving temporal references with the corresponding correct answers. |
Svenja Kenneweg; Jörg Deigmöller; Philipp Cimiano; Julian Eggert; | arxiv-cs.CL | 2025-05-02 |
| 51 | Simulating Question-answering Correctness with A Conditional Diffusion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a method called Diffusion-based Simulator (DSim), which takes advantage of diffusion to alleviate the bias accumulation. |
Ting Long; Li’ang Yin; Yi Chang; Wei Xia; Yong Yu; | www | 2025-04-30 |
| 52 | ConSens: Assessing Context Grounding in Open-book Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing evaluation methods, primarily based on the LLM-as-a-judge approach, face significant limitations, including biases, scalability issues, and dependence on costly external systems. To address these challenges, we propose a novel metric that contrasts the perplexity of the model response under two conditions: when the context is provided and when it is not. |
Ivan Vankov; Matyo Ivanov; Adriana Correia; Victor Botev; | arxiv-cs.CL | 2025-04-30 |
| 53 | Static or Dynamic: Towards Query-Adaptive Token Selection for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To alleviate this issue, existing works propose to compress video inputs, but usually overlooking the varying importance of static and dynamic information across different queries, leading to inefficient token usage within limited budgets. To tackle this, we propose a novel token selection strategy, EXPLORE-THEN-SELECT, that adaptively adjust static and dynamic information needed based on question requirements. |
Yumeng Shi; Quanyu Long; Wenya Wang; | arxiv-cs.CV | 2025-04-30 |
| 54 | Talk Before You Retrieve: Agent-Led Discussions for Better RAG in Medical QA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing medical RAG systems suffer from two key limitations: (1) a lack of modeling for human-like reasoning behaviors during information retrieval, and (2) reliance on suboptimal medical corpora, which often results in the retrieval of irrelevant or noisy snippets. To overcome these challenges, we propose Discuss-RAG, a plug-and-play module designed to enhance the medical QA RAG system through collaborative agent-based reasoning. |
XUANZHAO DONG et. al. | arxiv-cs.CL | 2025-04-29 |
| 55 | Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecom Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For domain-specific tasks, it is not clear if teacher or student model, or both, must be considered for domain adaptation. In this work, we study this problem from perspective of telecom domain Question-Answering (QA) task. |
Rishika Sen; Sujoy Roychowdhury; Sumit Soman; H. G. Ranjani; Srikhetra Mohanty; | arxiv-cs.CL | 2025-04-28 |
| 56 | VideoMultiAgents: A Multi-Agent Framework for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, many existing methods rely on feeding frame-level captions into a single model, making it difficult to adequately capture temporal and interactive contexts. To address this limitation, we introduce VideoMultiAgents, a framework that integrates specialized agents for vision, scene graph analysis, and text processing. |
NORIYUKI KUGO et. al. | arxiv-cs.CV | 2025-04-25 |
| 57 | A Comprehensive Survey of Knowledge-Based Vision Question Answering Systems: The Lifecycle of Knowledge in Visual Reasoning Task Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite substantial progress, no comprehensive survey currently exists that systematically organizes and reviews the existing KB-VQA methods. This survey aims to fill this gap by establishing a structured taxonomy of KB-VQA approaches, and categorizing the systems into main stages: knowledge representation, knowledge retrieval, and knowledge reasoning. |
Jiaqi Deng; Zonghan Wu; Huan Huo; Guandong Xu; | arxiv-cs.CV | 2025-04-24 |
| 58 | Credible Plan-driven RAG Method for Multi-hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, deviations in reasoning paths or errors in intermediate results, which are common in current RAG methods, may propagate and accumulate throughout the reasoning process, diminishing the accuracy of the answer to complex queries. To address this challenge, we propose the Plan-then-Act-and-Review (PAR RAG) framework, which is organized into three key stages: planning, act, and review, and aims to offer an interpretable and incremental reasoning paradigm for accurate and reliable multi-hop question answering by mitigating error propagation.PAR RAG initially applies a top-down problem decomposition strategy, formulating a comprehensive plan that integrates multiple executable steps from a holistic viewpoint. |
NINGNING ZHANG et. al. | arxiv-cs.CL | 2025-04-23 |
| 59 | TraveLLaMA: Facilitating Multi-modal Large Language Models to Understand Urban Scenes and Provide Travel Assistance Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present TraveLLaMA, a specialized multimodal language model designed for urban scene understanding and travel assistance. |
MENG CHU et. al. | arxiv-cs.CV | 2025-04-23 |
| 60 | FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present FinDER, an expert-generated dataset tailored for Retrieval-Augmented Generation (RAG) in finance. |
CHANYEOL CHOI et. al. | arxiv-cs.IR | 2025-04-22 |
| 61 | Efficient Document Retrieval with G-Retriever Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an enhanced approach that replaces the PCST method with an attention-based sub-graph construction technique, enabling more efficient and context-aware retrieval. |
Manthankumar Solanki; | arxiv-cs.LG | 2025-04-21 |
| 62 | A Hierarchical Framework for Measuring Scientific Paper Innovation Via Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose HSPIM, a hierarchical and training-free framework based on large language models (LLMs). |
Hongming Tan; Shaoxiong Zhan; Fengwei Jia; Hai-Tao Zheng; Wai Kin Chan; | arxiv-cs.CL | 2025-04-20 |
| 63 | FinSage: A Multi-aspect RAG System for Financial Filings Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose the FinSage framework as a solution, utilizing a multi-aspect RAG framework tailored for regulatory compliance analysis in multi-modal financial documents. |
XINYU WANG et. al. | arxiv-cs.IR | 2025-04-20 |
| 64 | Long-context Non-factoid Question Answering in Indic Languages Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study explores context-shortening techniques, including Open Information Extraction (OIE), coreference resolution, Answer Paragraph Selection (APS), and their combinations, to improve QA performance. |
Ritwik Mishra; Rajiv Ratn Shah; Ponnurangam Kumaraguru; | arxiv-cs.CL | 2025-04-18 |
| 65 | ChartQA-X: Generating Explanations for Charts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we address the challenge of providing explanations alongside answering questions about chart images. |
Shamanthak Hegde; Pooyan Fazli; Hasti Seifi; | arxiv-cs.CV | 2025-04-17 |
| 66 | LLM-as-a-Judge: Reassessing The Performance of LLMs in Extractive QA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we reassess the performance of QA models using LLM-as-a-judge across four reading comprehension QA datasets. |
Xanh Ho; Jiahao Huang; Florian Boudin; Akiko Aizawa; | arxiv-cs.CL | 2025-04-16 |
| 67 | Bridging The Semantic Gaps: Improving Medical VQA Consistency with LLM-Augmented Question Sets Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, linguistic variability in question phrasing often undermines the consistency of these systems. To address this challenge, we propose a Semantically Equivalent Question Augmentation (SEQA) framework, which leverages large language models (LLMs) to generate diverse yet semantically equivalent rephrasings of questions. |
Yongpei Ma; Pengyu Wang; Adam Dunn; Usman Naseem; Jinman Kim; | arxiv-cs.CV | 2025-04-16 |
| 68 | Mitigating LLM Hallucinations with Knowledge Graphs: A Case Study Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This research paper provides learning outcomes from a case study with LinkQ, an open-source natural language interface that was developed to combat hallucinations by forcing an LLM to query a knowledge graph (KG) for ground-truth data during question-answering (QA). |
Harry Li; Gabriel Appleby; Kenneth Alperin; Steven R Gomez; Ashley Suh; | arxiv-cs.HC | 2025-04-16 |
| 69 | Pre-training, Fine-tuning and Re-ranking: A Three-Stage Framework for Legal Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a three-stage (pre-training, fine-tuning and re-ranking) framework for legal QA (called PFR-LQA), which promotes the fine-grained text representation learning and boosts the performance of dense retrieval with the dual-encoder architecture. |
S. Ni; H. Cheng; M. Yang; | icassp | 2025-04-15 |
| 70 | MQAD: A Large-Scale Question Answering Dataset for Training Music Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces MQAD, a music QA dataset built on the Million Song Dataset (MSD), encompassing a rich array of musical features – including beat, chord, key, structure, instrument, and genre — across 270,000 tracks, featuring nearly 3 million diverse questions and captions. |
Z. OUYANG et. al. | icassp | 2025-04-15 |
| 71 | Electrocardiogram Report Generation and Question Answering Via Retrieval-Augmented Self-Supervised Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Interpreting electrocardiograms (ECGs) and generating comprehensive reports remain challenging tasks in cardiology, often requiring specialized expertise and significant time investment. To address these critical issues, we propose ECG-ReGen, a retrieval-based approach for ECG-to-text report generation and question answering. |
J. Tang; T. Xia; Y. Lu; C. Mascolo; A. Saeed; | icassp | 2025-04-15 |
| 72 | Benchmarking Biopharmaceuticals Retrieval-Augmented Generation Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce the Biopharmaceuticals Retrieval-Augmented Generation Evaluation (BRAGE) , the first benchmark tailored for evaluating LLMs’ Query and Reference Understanding Capability (QRUC) in the biopharmaceutical domain, available in English, French, German and Chinese. |
Hanmeng Zhong; Linqing Chen; Weilei Wang; Wentao Wu; | arxiv-cs.CL | 2025-04-15 |
| 73 | A Hierarchical Reasoning Framework for Complex Question Answering Over Knowledge Graph with Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To enhance model interpretability, reinforcement learning based methods are introduced. |
Z. Zhang; Z. Zhang; Y. Zhang; W. Zhao; | icassp | 2025-04-15 |
| 74 | Constraint-Awareness and Graph Reasoning for Temporal Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thirdly, a gap exists between the vector spaces of KG embeddings and question representations, and a simplistic hard concatenation or fusion of these two can lead to suboptimal solutions. To address these shortcomings, we propose a temporally aware QA method named Constraint-Awareness and Graph Reasoning. |
Z. Sun; K. Zhang; X. Zhang; J. Liu; | icassp | 2025-04-15 |
| 75 | Exploring The Role of Knowledge Graph-Based RAG in Japanese Medical Question Answering with Small-Scale LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Large language models (LLMs) perform well in medical QA, but their effectiveness in Japanese contexts is limited due to privacy constraints that prevent the use of commercial models like GPT-4 in clinical settings. |
YINGJIAN CHEN et. al. | arxiv-cs.CL | 2025-04-15 |
| 76 | Get Large Language Models Ready to Speak: A Late-fusion Approach for Speech Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce a text-to-speech (TTS) system powered by a fine-tuned Llama model, named TTS-Llama, that achieves state-of-the-art speech synthesis performance. |
M. Shen; | icassp | 2025-04-15 |
| 77 | Ai2 Scholar QA: Organized Literature Synthesis with Attribution Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce Ai2 Scholar QA, a free online scientific question answering application. |
AMANPREET SINGH et. al. | arxiv-cs.CL | 2025-04-15 |
| 78 | Visual Entity-Centric Prompting for Knowledge Retrieval in Knowledge-based VQA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a visual entity-centric prompting for knowledge retrieval (VEPR) to bridge the gap between the implicit and explicit knowledge driven by visual entities via large language models. |
J. YANG et. al. | icassp | 2025-04-15 |
| 79 | Multi-Prototype Grouping for Continual Learning in Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose ProtoGroup, a multi-prototype grouping continual learning framework for VQA, which groups prototypes based on their similarity to obtain more accurate and stable sample-invariant features. |
L. Zhang; Z. Mao; Y. Peng; Z. Fu; Y. Zhang; | icassp | 2025-04-15 |
| 80 | Speech Retrieval-Augmented Generation Without Automatic Speech Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While this cascaded pipeline has proven effective in many practical settings, ASR errors can propagate to the retrieval and generation steps. To overcome this limitation, we introduce SpeechRAG, a novel framework designed for open-question answering over spoken data. |
D. J. MIN et. al. | icassp | 2025-04-15 |
| 81 | Bridging Neural and Symbolic Reasoning: A Dual-System Framework for Interpretable Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, LLMs often lack transparency in their reasoning processes and struggle with hallucination. To overcome these challenges, we propose Dual-NeSy, a Dual-system framework that integrates Neural networks with Symbolic logic for interpretable question answering. |
J. Shi; X. Ding; H. Zhao; T. Liu; B. Qin; | icassp | 2025-04-15 |
| 82 | Leveraging Chain of Thought Towards Empathetic Spoken Dialogue Without Corresponding Question-Answering Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In response, we propose a novel approach that circumvents the need for question-answering data, called Listen, Perceive, and Express (LPE). |
J. Xie; | icassp | 2025-04-15 |
| 83 | Audiopedia: Audio QA with Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce Audiopedia, a novel task called Audio Question Answering with Knowledge, which requires both audio comprehension and external knowledge reasoning. |
A. S. Penamakuri; K. Chhatre; A. Jain; | icassp | 2025-04-15 |
| 84 | A Continual Learning Approach for Embodied Question Answering with Generative Adversarial Imitation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we proposed a continual learning method based on generative adversarial imitation learning and self-supervision to support the agent when facing unseen environments. |
X. ZENG et. al. | icassp | 2025-04-15 |
| 85 | SiQA: A Large Multi-Modal Question Answering Model for Structured Images Based on RAG Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose SiQA, a knowledge construction and Retrieval-Augmented Generation(RAG)-based multimodal Question-Answering model designed for Structured Images. |
J. Liu; Y. Tao; F. Wang; H. Li; X. Qin; | icassp | 2025-04-15 |
| 86 | AskQE: Question Answering As Automatic Evaluation for Machine Translation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce AskQE, a question generation and answering framework designed to detect critical MT errors and provide actionable feedback, helping users decide whether to accept or reject MT outputs even without the knowledge of the target language. |
Dayeon Ki; Kevin Duh; Marine Carpuat; | arxiv-cs.CL | 2025-04-15 |
| 87 | Seek and Solve Reasoning for Table Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper reveals that the reasoning process during task simplification may be more valuable than the simplified tasks themselves and aims to improve TQA performance by leveraging LLMs’ reasoning capabilities. We propose a Seek-and-Solve pipeline that instructs the LLM to first seek relevant information and then answer questions, integrating these two stages at the reasoning level into a coherent Seek-and-Solve Chain of Thought (SS-CoT). |
R. Jiang; C. Wang; W. Deng; | icassp | 2025-04-15 |
| 88 | HD-RAG: Retrieval-Augmented Generation for Hybrid Documents Containing Text and Hierarchical Tables Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new large-scale dataset, DocRAGLib, specifically designed for the question answering (QA) task scenario under Hybrid Document RAG. |
Chi Zhang; Qiyang Chen; | arxiv-cs.IR | 2025-04-13 |
| 89 | Composable NLP Workflows for BERT-based Ranking and QA System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we built an end-to-end Ranking and Question-Answering (QA) system using Forte, a toolkit that makes composable NLP pipelines. |
Gaurav Kumar; Murali Mohana Krishna Dandu; | arxiv-cs.CL | 2025-04-12 |
| 90 | Knowledge Graph-extended Retrieval Augmented Generation for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Ideally, an AI system should be both robust to missing facts as well as easy to communicate with. This paper proposes such a system that integrates LLMs and KGs without requiring training, ensuring adaptability across different KGs with minimal human effort. |
Jasper Linders; Jakub M. Tomczak; | arxiv-cs.LG | 2025-04-11 |
| 91 | VLMT: Vision-Language Multimodal Transformer for Multimodal Multi-hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing methods for Multimodal Multi-hop Question Answering (MMQA) often suffer from limited reasoning capabilities, reliance on modality conversion, and inadequate alignment between visual and textual representations. To address these limitations, this paper introduces Vision-Language Multimodal Transformer (VLMT), a unified architecture that integrates a transformer-based vision encoder with a sequence-to-sequence language model. |
Qi Zhi Lim; Chin Poo Lee; Kian Ming Lim; Kalaiarasi Sonai Muthu Anbananthen; | arxiv-cs.CV | 2025-04-11 |
| 92 | How Can Objects Help Video-Language Understanding? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We aim to answer the question: how can objects help video-language understanding in MLLMs? |
Zitian Tang; Shijie Wang; Junho Cho; Jaewook Yoo; Chen Sun; | arxiv-cs.CV | 2025-04-10 |
| 93 | REVEAL: Relation-based Video Representation Learning for Video-Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Video-Question-Answering (VideoQA) comprises the capturing of complex visual relation changes over time, remaining a challenge even for advanced Video Language Models (VLM), i.a., because of the need to represent the visual content to a reasonably sized input for those models. To address this problem, we propose RElation-based Video rEpresentAtion Learning (REVEAL), a framework designed to capture visual relation information by encoding them into structured, decomposed representations. |
Sofian Chaybouti; Walid Bousselham; Moritz Wolter; Hilde Kuehne; | arxiv-cs.CV | 2025-04-07 |
| 94 | Evaluating Knowledge Graph Based Retrieval Augmented Generation Methods Under Knowledge Incompleteness Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we systematically evaluate KG-RAG methods under incomplete KGs by removing triples using different methods and analyzing the resulting effects. |
DONGZHUORAN ZHOU et. al. | arxiv-cs.AI | 2025-04-07 |
| 95 | Probing The Visualization Literacy of Vision Language Models: The Good, The Bad, and The Ugly Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Vision Language Models (VLMs) demonstrate promising chart comprehension capabilities. |
Lianghan Dong; Anamaria Crisan; | arxiv-cs.HC | 2025-04-07 |
| 96 | Simplifying Data Integration: SLM-Driven Systems for Unified Semantic Queries Across Heterogeneous Databases Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a novel Small Language Model(SLM)-driven system that synergizes advancements in lightweight Retrieval-Augmented Generation (RAG) and semantic-aware data structuring to enable efficient, accurate, and scalable query resolution across diverse data formats. |
Teng Lin; | arxiv-cs.DB | 2025-04-07 |
| 97 | A Lightweight Large Vision-language Model for Multimodal Medical Images Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a lightweight, multimodal VQA model integrating BiomedCLIP for image feature extraction and LLaMA-3 for text processing. |
Belal Alsinglawi; Chris McCarthy; Sara Webb; Christopher Fluke; Navid Toosy Saidy; | arxiv-cs.CV | 2025-04-07 |
| 98 | Collab-RAG: Boosting Retrieval-Augmented Generation for Complex Question Answering Via White-Box and Black-Box LLM Collaboration Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce Collab-RAG, a collaborative training framework that leverages mutual enhancement between a white-box small language model (SLM) and a blackbox large language model (LLM) for RAG. |
RAN XU et. al. | arxiv-cs.CL | 2025-04-07 |
| 99 | Advancing Egocentric Video Question Answering with Multimodal Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce QaEgo4Dv2 to mitigate annotation noise in QaEgo4D, enabling more reliable comparison. |
Alkesh Patel; Vibhav Chitalia; Yinfei Yang; | arxiv-cs.CV | 2025-04-06 |
| 100 | The Point, The Vision and The Text: Does Point Cloud Boost Spatial Reasoning of Large Language Models? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite some promising results, the role of point clouds in 3D spatial reasoning remains under-explored. In this work, we comprehensively evaluate and analyze these models to answer the research question: \textit{Does point cloud truly boost the spatial reasoning capacities of 3D LLMs?} |
WEICHEN ZHANG et. al. | arxiv-cs.CV | 2025-04-06 |
| 101 | UniRVQA: A Unified Framework for Retrieval-Augmented Vision Question Answering Via Self-Reflective Joint Training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To bridge the gap, we propose a Unified Retrieval-Augmented VQA framework (UniRVQA). |
JIAQI DENG et. al. | arxiv-cs.CV | 2025-04-05 |
| 102 | QIRL: Boosting Visual Question Answering Via Optimized Question-Image Relation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Second, they do not assess the relevance between the input question and image during inference, as no prior work has examined the degree of input relevance in debiasing studies. Motivated by these limitations, we propose a novel framework, Optimized Question-Image Relation Learning (QIRL), which employs a generation-based self-supervised learning strategy. |
QUANXING XU et. al. | arxiv-cs.CV | 2025-04-04 |
| 103 | Hierarchical Modeling for Medical Visual Question Answering with Cross-Attention Fusion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: (2) Excessive reliance on implicit learning in Transformer-based cross-modal self-attention fusion methods, which obscures crucial local semantic correlations in medical scenarios. To address these issues, this study proposes a HiCA-VQA method, including two modules: Hierarchical Prompting for fine-grained medical questions and Hierarchical Answer Decoders. |
Junkai Zhang; Bin Li; Shoujun Zhou; Yue Du; | arxiv-cs.CV | 2025-04-03 |
| 104 | Single-Pass Document Scanning for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a single-pass document scanning approach that processes the entire text in linear time, preserving global coherence while deciding which sentences are most relevant to the query. |
WEILI CAO et. al. | arxiv-cs.CL | 2025-04-03 |
| 105 | Leveraging Static Relationships for Intra-Type and Inter-Type Message Passing in Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although methods based on static relationship reasoning have made certain progress, there are still deficiencies in the accuracy of static relationship recognition and representation, and they have not fully utilized the static relationship information in videos for in-depth reasoning and analysis. Therefore, this paper proposes a reasoning method for intra-type and inter-type message passing based on static relationships. |
Lili Liang; Guanglu Sun; | arxiv-cs.CV | 2025-04-03 |
| 106 | GeoRAG: A Question-Answering Approach from A Geographical Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study presents GeoRAG, a knowledge-enhanced QA framework integrating domain-specific fine-tuning and prompt engineering with Retrieval-Augmented Generation (RAG) technology to enhance geographic knowledge retrieval accuracy and user interaction. |
JIAN WANG et. al. | arxiv-cs.IR | 2025-04-02 |
| 107 | GTR: Graph-Table-RAG for Cross-Table Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Beyond pure text, a substantial amount of knowledge is stored in tables. In real-world scenarios, user questions often require retrieving answers that are distributed across … |
JIARU ZOU et. al. | arxiv-cs.CL | 2025-04-02 |
| 108 | Visual Environment-Interactive Planning for Embodied Complex-Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Considering multi-step planning, the framework for formulating plans in a sequential manner is proposed in this paper. |
Ning Lan; Baoshan Ou; Xuemei Xie; Guangming Shi; | arxiv-cs.RO | 2025-04-01 |
| 109 | Biomedical Question Answering Via Multi-Level Summarization on A Local Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel method that utilizes propositional claims to construct a local knowledge graph from retrieved documents. |
Lingxiao Guan; Yuanhao Huang; Jie Liu; | arxiv-cs.CL | 2025-04-01 |
| 110 | FortisAVQA and MAVEN: A Benchmark Dataset and Debiasing Framework for Robust Multimodal Reasoning Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Audio-Visual Question Answering (AVQA) is a challenging multimodal reasoning task requiring intelligent systems to answer natural language queries based on paired audio-video … |
JIE MA et. al. | arxiv-cs.MM | 2025-04-01 |
| 111 | Are You Really Listening? Boosting Perceptual Awareness in Music-QA Benchmarks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These findings suggest existing benchmarks predominantly assess reasoning abilities rather than audio perception. To overcome this challenge, we present RUListening: Robust Understanding through Listening, a framework that enhances perceptual evaluation in Music-QA benchmarks. |
Yongyi Zang; Sean O’Brien; Taylor Berg-Kirkpatrick; Julian McAuley; Zachary Novack; | arxiv-cs.SD | 2025-03-31 |
| 112 | Question-Aware Knowledge Graph Prompting for Enhancing Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Moreover, in MCQA tasks, the absence of relevant KG knowledge for certain answer options remains a significant challenge. To address these issues, we propose Question-Aware Knowledge Graph Prompting (QAP), which incorporates question embeddings into GNN aggregation to dynamically assess KG relevance. |
Haochen Liu; Song Wang; Chen Chen; Jundong Li; | arxiv-cs.CL | 2025-03-30 |
| 113 | Memory-Aware and Uncertainty-Guided Retrieval for Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While Retrieval-Augmented Generation (RAG) has made progress in this area, existing methods often suffer from two key limitations: (1) fixed or overly frequent retrieval steps, and (2) ineffective use of previously retrieved knowledge. We propose MIND (Memory-Informed and INteractive Dynamic RAG), a framework that addresses these challenges through: (i) prompt-based entity extraction to identify reasoning-relevant elements, (ii) dynamic retrieval triggering based on token-level entropy and attention signals, and (iii) memory-aware filtering, which stores high-confidence facts across reasoning steps to enable consistent multi-hop generation. |
Yuelyu Ji; Rui Meng; Zhuochun Li; Daqing He; | arxiv-cs.CL | 2025-03-29 |
| 114 | FReM: A Flexible Reasoning Mechanism for Balancing Quick and Slow Thinking in Long-Context Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Quick thinking usually relies on pattern matching rather than truly understanding the query logic, which misses proper understanding. To address these issues, we propose FReM: Flexible Reasoning Mechanism, a method that adjusts reasoning depth according to the complexity of each question. |
ZHENGYI ZHAO et. al. | arxiv-cs.CL | 2025-03-29 |
| 115 | Can DeepSeek Reason Like A Surgeon? An Empirical Evaluation for Vision-Language Understanding in Robotic-Assisted Surgery Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we investigate the dialogue capabilities of the DeepSeek model in robotic surgery scenarios, focusing on tasks such as Single Phrase QA, Visual QA, and Detailed Description. |
BOYI MA et. al. | arxiv-cs.CV | 2025-03-29 |
| 116 | Unveiling The Mist Over 3D Vision-Language Understanding: Object-centric Evaluation with Chain-of-Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To unveil the mist, we propose Beacon3D, a benchmark for 3D-VL grounding and QA tasks, delivering a perspective shift in the evaluation of 3D-VL understanding. |
JIANGYONG HUANG et. al. | arxiv-cs.CV | 2025-03-28 |
| 117 | Preference-based Learning with Retrieval Augmented Generation for Conversational Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This work presents PRAISE, a pipeline-based approach for ConvQA that trains LLM adapters for each of the three subtasks. |
Magdalena Kaiser; Gerhard Weikum; | arxiv-cs.CL | 2025-03-28 |
| 118 | ReaRAG: Knowledge-guided Reasoning Enhances Factuality of Large Reasoning Models with Iterative Retrieval Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While recent works equip reinforcement learning (RL)-based LRMs with retrieval capabilities, they suffer from overthinking and lack robustness in reasoning, reducing their effectiveness in question answering (QA) tasks. To address this, we propose ReaRAG, a factuality-enhanced reasoning model that explores diverse queries without excessive iterations. |
ZHICHENG LEE et. al. | arxiv-cs.CL | 2025-03-27 |
| 119 | How Well Can Vison-Language Models Understand Humans’ Intention? An Open-ended Theory of Mind Question Evaluation Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose an open-ended question framework to evaluate VLMs’ performance across diverse categories of ToM tasks. |
Ximing Wen; Mallika Mainali; Anik Sen; | arxiv-cs.CV | 2025-03-27 |
| 120 | AskSport: Web Application for Sports Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces AskSport, a question-answering web application about sports. |
Enzo B Onofre; Leonardo M P Moraes; Cristina D Aguiar; | arxiv-cs.AI | 2025-03-26 |
| 121 | DomainCQA: Crafting Expert-Level QA from Domain-Specific Charts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, current benchmarks focus primarily on the evaluation of general-purpose CQA but fail to adequately capture domain-specific challenges. We introduce DomainCQA, a systematic methodology for constructing domain-specific CQA benchmarks, and demonstrate its effectiveness by developing AstroChart, a CQA benchmark in the field of astronomy. |
LING ZHONG et. al. | arxiv-cs.CL | 2025-03-25 |
| 122 | VGAT: A Cancer Survival Analysis Framework Transitioning from Generative Visual Question Answering to Genomic Reconstruction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To enable survival prediction using only whole-slide images (WSI), we propose the Visual-Genomic Answering-Guided Transformer (VGAT), a framework integrating Visual Question Answering (VQA) techniques for genomic modality reconstruction. |
ZIZHI CHEN et. al. | arxiv-cs.CV | 2025-03-25 |
| 123 | Improved Alignment of Modalities in Large Vision Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose four training stages for aligning the vision model with the language model, in other words, the language model is given an ability to process visual inputs. |
Kartik Jangra; Aman Kumar Singh; Yashwani Mann; Geetanjali Rathee; | arxiv-cs.CV | 2025-03-25 |
| 124 | A Survey of Large Language Model Agents for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper surveys the development of large language model (LLM)-based agents for question answering (QA). |
Murong Yue; | arxiv-cs.CL | 2025-03-24 |
| 125 | MAGIC-VQA: Multimodal And Grounded Inference with Commonsense Knowledge for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Visual Question Answering (VQA) requires reasoning across visual and textual modalities, yet Large Vision-Language Models (LVLMs) often lack integrated commonsense knowledge, limiting their robustness in real-world scenarios. To address this, we introduce MAGIC-VQA, a novel framework that enhances VQA by systematically integrating commonsense knowledge with LVLMs. |
Shuo Yang; Siwen Luo; Soyeon Caren Han; Eduard Hovy; | arxiv-cs.CL | 2025-03-24 |
| 126 | SUNAR: Semantic Uncertainty Based Neighborhood Aware Retrieval for Complex QA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce SUNAR, a novel approach that leverages LLMs to guide a Neighborhood Aware Retrieval process. |
V Venktesh; Mandeep Rathee; Avishek Anand; | arxiv-cs.IR | 2025-03-23 |
| 127 | 4D-Bench: Benchmarking Multi-modal Large Language Models for 4D Object Understanding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce 4D-Bench, the first benchmark to evaluate the capabilities of MLLMs in 4D object understanding, featuring tasks in 4D object Question Answering (4D object QA) and 4D object captioning. |
WENXUAN ZHU et. al. | arxiv-cs.CV | 2025-03-22 |
| 128 | Joint Extraction Matters: Prompt-Based Visual Question Answering for Multi-Field Document Information Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper investigates the merits of extracting multiple fields jointly versus separately. |
Mengsay Loem; Taiju Hosaka; | arxiv-cs.CL | 2025-03-21 |
| 129 | ETVA: Evaluation of Text-to-Video Alignment Via Fine-grained Question Generation and Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing text-to-video alignment metrics like CLIPScore only generate coarse-grained scores without fine-grained alignment details, failing to align with human preference. To address this limitation, we propose ETVA, a novel Evaluation method of Text-to-Video Alignment via fine-grained question generation and answering. |
KAISI GUAN et. al. | arxiv-cs.CV | 2025-03-21 |
| 130 | MTBench: A Multimodal Time Series Benchmark for Temporal Reasoning and Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While multimodal learning has gained traction, existing multimodal time-series datasets fall short in evaluating cross-modal reasoning and complex question answering, which are essential for capturing complex interactions between narrative information and temporal patterns. To bridge this gap, we introduce Multimodal Time Series Benchmark (MTBench), a large-scale benchmark designed to evaluate large language models (LLMs) on time series and text understanding across financial and weather domains. |
JIALIN CHEN et. al. | arxiv-cs.CL | 2025-03-21 |
| 131 | PVChat: Personalized Video Chat with One-Shot Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we introduce an automated augmentation pipeline that synthesizes identity-preserving positive samples and retrieves hard negatives from existing video corpora, generating a diverse training dataset with four QA types: existence, appearance, action, and location inquiries. |
YUFEI SHI et. al. | arxiv-cs.CV | 2025-03-21 |
| 132 | Agentic Keyframe Search for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address it, we propose Agentic Keyframe Search (AKeyS), a simple yet powerful algorithm for identifying keyframes in the VideoQA task. |
Sunqi Fan; Meng-Hao Guo; Shuojin Yang; | arxiv-cs.CV | 2025-03-20 |
| 133 | Typed-RAG: Type-aware Multi-Aspect Decomposition for Non-Factoid Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Unlike factoid questions, non-factoid questions (NFQs) lack definitive answers and require synthesizing information from multiple sources across various reasoning dimensions. To address these limitations, we introduce Typed-RAG, a type-aware multi-aspect decomposition framework within the RAG paradigm for NFQA. |
DongGeon Lee; Ahjeong Park; Hyeri Lee; Hyeonseo Nam; Yunho Maeng; | arxiv-cs.CL | 2025-03-20 |
| 134 | MKG-Rank: Enhancing Large Language Models with Knowledge Graph for Multilingual Medical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Large Language Models (LLMs) have shown remarkable progress in medical question answering (QA), yet their effectiveness remains predominantly limited to English due to imbalanced multilingual training data and scarce medical resources for low-resource languages. To address this critical language gap in medical QA, we propose Multilingual Knowledge Graph-based Retrieval Ranking (MKG-Rank), a knowledge graph-enhanced framework that enables English-centric LLMs to perform multilingual medical QA. |
FEIYANG LI et. al. | arxiv-cs.CL | 2025-03-20 |
| 135 | GraspCoT: Integrating Physical Property Reasoning for 6-DoF Grasping Under Flexible Language Instructions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose GraspCoT, a 6-DoF grasp detection framework that integrates a Chain-of-Thought (CoT) reasoning mechanism oriented to physical properties, guided by auxiliary question-answering (QA) tasks. |
XIAOMENG CHU et. al. | arxiv-cs.RO | 2025-03-20 |
| 136 | Bias Evaluation and Mitigation in Retrieval-Augmented Medical Question-Answering Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study systematically evaluates demographic biases within medical RAG pipelines across multiple QA benchmarks, including MedQA, MedMCQA, MMLU, and EquityMedQA. |
Yuelyu Ji; Hang Zhang; Yanshan Wang; | arxiv-cs.CL | 2025-03-19 |
| 137 | Neuro Symbolic Knowledge Reasoning for Procedural Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a new video question-answering (VQA) dataset that challenges models to leverage procedural knowledge for complex reasoning. |
THANH-SON NGUYEN et. al. | arxiv-cs.CV | 2025-03-19 |
| 138 | AutoDrive-QA- Automated Generation of Multiple-Choice Questions for Autonomous Driving Datasets Using Large Vision-Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In autonomous driving, open-ended question answering often suffers from unreliable evaluations because freeform responses require either complex metrics or subjective human judgment. To address this challenge, we introduce AutoDrive-QA, an automatic pipeline that converts existing driving QA datasets (including DriveLM, NuScenes-QA, and LingoQA) into a structured multiple-choice question (MCQ) format. |
Boshra Khalili; Andrew W. Smyth; | arxiv-cs.CV | 2025-03-19 |
| 139 | Right Answer, Wrong Score: Uncovering The Inconsistencies of LLM Evaluation in Multiple-Choice Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we shed light on the inconsistencies of MCQA evaluation strategies, which can lead to inaccurate and misleading model comparisons. |
FRANCESCO MARIA MOLFESE et. al. | arxiv-cs.CL | 2025-03-19 |
| 140 | EIAD: Explainable Industrial Anomaly Detection Via Multi-Modal Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, the application of large multi-modal models in IAD remains in its infancy, facing challenges in balancing question-answering (QA) performance and mask-based grounding capabilities, often owing to overfitting during the fine-tuning process. To address these challenges, we propose a novel approach that introduces a dedicated multi-modal defect localization module to decouple the dialog functionality from the core feature extraction. |
Zongyun Zhang; Jiacheng Ruan; Xian Gao; Ting Liu; Yuzhuo Fu; | arxiv-cs.AI | 2025-03-18 |
| 141 | Elevating Visual Question Answering Through Implicitly Learned Reasoning Pathways in LVLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Large Vision-Language Models (LVLMs) have shown remarkable progress in various multimodal tasks, yet they often struggle with complex visual reasoning that requires multi-step inference. To address this limitation, we propose MF-SQ-LLaVA, a novel approach that enhances LVLMs by enabling implicit self-questioning through end-to-end training. |
Liu Jing; Amirul Rahman; | arxiv-cs.CV | 2025-03-18 |
| 142 | Synthetic Clarification and Correction Dialogues About Data-Centric Tasks — A Teacher-Student Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we develop a novel framework for synthetically generating controlled, multi-turn conversations between a user and AI assistant for the task of table-based question answering, which can be generated from an existing dataset with fully specified table QA examples for any target domain. |
Christian Poelitz; Nick McKenna; | arxiv-cs.CL | 2025-03-18 |
| 143 | VideoMind: A Chain-of-LoRA Agent for Long Video Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce VideoMind, a novel video-language agent designed for temporal-grounded video understanding. |
Ye Liu; Kevin Qinghong Lin; Chang Wen Chen; Mike Zheng Shou; | arxiv-cs.CV | 2025-03-17 |
| 144 | ClimaQA: An Automated Evaluation Framework for Climate Question Answering Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As a result, we present *ClimaQA-Gold*, an expert-annotated benchmark dataset alongside *ClimaQA-Silver*, a large-scale, comprehensive synthetic QA dataset for climate science. |
VEERAMAKALI VIGNESH MANIVANNAN et. al. | iclr | 2025-03-17 |
| 145 | CG-Bench: Clue-grounded Question Answering Benchmark for Long Video Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Due to the limitations of MCQ evaluations and the advanced reasoning abilities of MLLMs, models can often answer correctly by combining short video insights with elimination, without truly understanding the content. To bridge this gap, we introduce CG-Bench, a benchmark for clue-grounded question answering in long videos. |
GUO CHEN et. al. | iclr | 2025-03-17 |
| 146 | Streaming Video Question-Answering with In-context Video KV-Cache Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose ReKV, a novel training-free approach that enables efficient streaming video question-answering (StreamingVQA), by seamlessly integrating with existing Video Large Language Models (Video-LLMs). |
SHANGZHE DI et. al. | iclr | 2025-03-17 |
| 147 | SVBench: A Benchmark with Temporal Multi-Turn Dialogues for Streaming Video Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Current benchmarks for video understanding typically emphasize isolated single-instance text inputs and fail to evaluate the capacity to sustain temporal reasoning throughout the entire duration of video streams. To address these limitations, we introduce SVBench, a pioneering benchmark with temporal multi-turn question-answering chains specifically designed to thoroughly assess the capabilities of streaming video understanding of current LVLMs. |
ZHENYU YANG et. al. | iclr | 2025-03-17 |
| 148 | Modeling Future Conversation Turns to Teach LLMs to Ask Clarifying Questions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing LLMs often respond by presupposing a single interpretation of such ambiguous requests, frustrating users who intended a different interpretation. We speculate this is caused by current preference data labeling practice, where LLM responses are evaluated only on their prior contexts. To address this, we assign preference labels by simulating their expected outcomes in future turns. |
Michael JQ Zhang; W. Bradley Knox; Eunsol Choi; | iclr | 2025-03-17 |
| 149 | Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These challenges often arise due to the complexity and ambiguity present in longer texts. To enhance the performance of LLMs in such scenarios, we introduce the Long Question Coreference Adaptation (LQCA) method. |
YANMING LIU et. al. | iclr | 2025-03-17 |
| 150 | MLLMs Know Where to Look: Training-free Perception of Small Visual Details with Multimodal LLMs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we study whether MLLMs can perceive small visual details as effectively as large ones when answering questions about images. |
Jiarui Zhang; Mahyar Khayatkhoei; Prateek Chhikara; Filip Ilievski; | iclr | 2025-03-17 |
| 151 | VITED: Video Temporal Evidence Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a framework to enhance existing VideoQA datasets with evidence reasoning chains, automatically constructed by searching for optimal intervals of interest in the video with supporting evidence, that maximizes the likelihood of answering a given question. |
Yujie Lu; Yale Song; William Wang; Lorenzo Torresani; Tushar Nagarajan; | arxiv-cs.CV | 2025-03-17 |
| 152 | CofCA: A STEP-WISE Counterfactual Multi-hop QA Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, current factual Multi-hop QA (MHQA) benchmarks are annotated on open-source corpora such as Wikipedia, although useful for multi-step reasoning evaluation, they show limitations due to the potential data contamination in LLMs’ pre-training stage. To address these issues, we introduce the Step-wise and Counterfactual benchmark (CofCA), a novel evaluation benchmark consisting of factual data and counterfactual data that reveals LLMs’ real reasoning abilities on multi-step reasoning and reasoning chain evaluation. |
Jian Wu; Linyi Yang; Zhen Wang; Manabu Okumura; Yue Zhang; | iclr | 2025-03-17 |
| 153 | Long-VMNet: Accelerating Long-Form Video Understanding Via Fixed Memory Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Yet, traditional approaches demand substantial computing power and are often bottlenecked by GPU memory. To tackle this issue, we present Long-Video Memory Network, Long-VMNet, a novel video understanding method that employs a fixed-size memory representation to store discriminative patches sampled from the input video. |
Saket Gurukar; Asim Kadav; | arxiv-cs.CV | 2025-03-17 |
| 154 | Chain-of-Action: Faithful and Multimodal Question Answering Through Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present a Chain-of-Action (CoA) framework for multimodal and retrieval-augmented Question-Answering (QA). |
Zhenyu Pan; Haozheng Luo; Manling Li; Han Liu; | iclr | 2025-03-17 |
| 155 | Shot2Story: A New Benchmark for Comprehensive Understanding of Multi-shot Videos Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present a new multi-shot video understanding benchmark \dataset with detailed shot-level captions, comprehensive video summaries and question-answering pairs. |
Mingfei Han; Linjie Yang; Xiaojun Chang; Lina Yao; Heng Wang; | iclr | 2025-03-17 |
| 156 | Visual Haystacks: A Vision-Centric Needle-In-A-Haystack Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Towards a solution, we introduce MIRAGE (Multi-Image Retrieval Augmented Generation), an open-source, lightweight visual-RAG framework that processes up to 10k images on a single 40G A100 GPU—far surpassing the 1k-image limit of contemporary models. |
TSUNG-HAN WU et. al. | iclr | 2025-03-17 |
| 157 | QA-Calibration of Language Model Confidence Scores Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We argue, however, that this standard (average-case) notion of calibration is difficult to interpret for decision-making in generative QA. To address this, we generalize the standard notion of average calibration and introduce QA-calibration, which ensures calibration holds across different question-and-answer groups. |
Putra Manggala; Atalanti A. Mastakouri; Elke Kirschbaum; Shiva Kasiviswanathan; Aaditya Ramdas; | iclr | 2025-03-17 |
| 158 | HIS-GPT: Towards 3D Human-In-Scene Multimodal Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a new task to benchmark human-in-scene understanding for embodied agents: Human-In-Scene Question Answering (HIS-QA). |
Jiahe Zhao; Ruibing Hou; Zejie Tian; Hong Chang; Shiguang Shan; | arxiv-cs.CV | 2025-03-17 |
| 159 | Generalization V.s. Memorization: Tracing Language Models’ Capabilities Back to Pretraining Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To effectively capture task-specific pretraining data frequency, we propose a novel task-gram language model, which is built by counting the co-occurrence of semantically related $n$-gram pairs from task inputs and outputs in the pretraining corpus. |
XINYI WANG et. al. | iclr | 2025-03-17 |
| 160 | General Table Question Answering Via Answer-Formula Joint Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we construct a large Formula-annotated TableQA dataset \texttt{FromulaQA} from existing datasets. |
Zhongyuan Wang; Richong Zhang; Zhijie Nie; | arxiv-cs.CL | 2025-03-15 |
| 161 | Reinforcement Learning Outperforms Supervised Fine-Tuning: A Case Study on Audio Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We leverage the group relative policy optimization (GRPO) algorithm to Qwen2-Audio-7B-Instruct, and our experiments demonstrated state-of-the-art performance on the MMAU Test-mini benchmark, achieving an accuracy rate of 64.5%. The main findings in this technical report are as follows: 1) The GRPO algorithm can be effectively applied to large audio language models (LALMs), even when the model has only 8.2B parameters; 2) With only 38k post-training samples, RL significantly outperforms supervised fine-tuning (SFT), indicating that RL-based approaches can be effective without large datasets; 3) The explicit reasoning process has not shown significant benefits for AQA tasks, and how to efficiently utilize deep thinking remains an open question for further research; 4) LALMs still lag far behind humans auditory-language reasoning, suggesting that the RL-based approaches warrant further exploration. |
GANG LI et. al. | arxiv-cs.SD | 2025-03-14 |
| 162 | Beyond The Destination: A Novel Benchmark for Exploration-Aware Embodied Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To improve exploration efficiency, we propose Fine-EQA, a hybrid exploration model that integrates frontier-based and goal-oriented navigation to guide agents toward task-relevant regions more effectively. |
KAIXUAN JIANG et. al. | arxiv-cs.CV | 2025-03-14 |
| 163 | RePanda: Pandas-powered Tabular Verification and Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce RePanda, a structured fact verification approach that translates claims into executable pandas queries, enabling interpretable and verifiable reasoning. |
Atoosa Malemir Chegini; Keivan Rezaei; Hamid Eghbalzadeh; Soheil Feizi; | arxiv-cs.LG | 2025-03-14 |
| 164 | BIMBA: Selective-Scan Compression for Long-Range Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce BIMBA, an efficient state-space model to handle long-form videos. |
Md Mohaiminul Islam; Tushar Nagarajan; Huiyu Wang; Gedas Bertasius; Lorenzo Torresani; | arxiv-cs.CV | 2025-03-12 |
| 165 | PlainQAFact: Automatic Factuality Evaluation Metric for Biomedical Plain Language Summaries Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing factuality evaluation methods, such as entailment- and question-answering-based (QA), struggle with plain language summary (PLS) generation due to elaborative explanation phenomenon, which introduces external content (e.g., definitions, background, examples) absent from the source document to enhance comprehension. To address this, we introduce PlainQAFact, a framework trained on a fine-grained, human-annotated dataset PlainFact, to evaluate the factuality of both source-simplified and elaboratively explained sentences. |
Zhiwen You; Yue Guo; | arxiv-cs.CL | 2025-03-11 |
| 166 | Gradient-guided Attention Map Editing: Towards Efficient Contextual Hallucination Mitigation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This typically occurs because these models tend to prioritize self-generated content over the input context, causing them to disregard pertinent details. To address this challenge, we introduce a novel method called Guided Attention Map Editing (GAME), which dynamically adjusts attention maps to improve contextual relevance. |
YU WANG et. al. | arxiv-cs.CL | 2025-03-11 |
| 167 | MapQA: Open-domain Geospatial Question Answering on Map Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: A major challenge in scaling geospatial QA datasets for reasoning lies in the complexity of geospatial relationships, which require integrating spatial structures, topological dependencies, and multi-hop reasoning capabilities that most text-based QA datasets lack. To address these limitations, we introduce MapQA, a novel dataset that not only provides question-answer pairs but also includes the geometries of geo-entities referenced in the questions. |
Zekun Li; Malcolm Grossman; Mihir Kulkarni; Muhao Chen; Yao-Yi Chiang; | arxiv-cs.CL | 2025-03-10 |
| 168 | Talking to GDELT Through Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work we study various Retrieval Augmented Regeneration (RAG) approaches to gain an understanding of the strengths and weaknesses of each approach in a question-answering analysis. |
AUDUN MYERS et. al. | arxiv-cs.IR | 2025-03-10 |
| 169 | ReAgent: Reversible Multi-Agent Reasoning for Knowledge-Enhanced Multi-Hop QA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces ReAgent: a Reversible multi-Agent collaborative framework augmented with explicit backtracking mechanisms, enabling reversible multi-hop reasoning. |
ZHAO XINJIE et. al. | arxiv-cs.AI | 2025-03-10 |
| 170 | VisualSimpleQA: A Benchmark for Decoupled Evaluation of Large Vision-Language Models in Fact-Seeking Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Current multimodal fact-seeking benchmarks primarily focus on comparing model outputs to ground truth answers, providing limited insights into the performance of modality-specific modules. To bridge this gap, we introduce VisualSimpleQA, a multimodal fact-seeking benchmark with two key features. |
YANLING WANG et. al. | arxiv-cs.CL | 2025-03-09 |
| 171 | Towards Fine-Grained Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing datasets exhibit gaps in temporal and spatial granularity, which consequently limits the capabilities of existing VideoQA methods. This paper introduces the Multi-Object Multi-Actor Question Answering (MOMA-QA) dataset, which is designed to address these shortcomings by emphasizing temporal localization, spatial relationship reasoning, and entity-centric queries. |
WEI DAI et. al. | arxiv-cs.CV | 2025-03-09 |
| 172 | Correctness Coverage Evaluation for Medical Multiple-Choice Question Answering Based on The Enhanced Conformal Prediction Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes an enhanced CP framework for medical multiple-choice question-answering (MCQA) tasks. |
Yusong Ke; Hongru Lin; Yuting Ruan; Junya Tang; Li Li; | arxiv-cs.CL | 2025-03-07 |
| 173 | Evaluating Answer Reranking Strategies in Time-sensitive Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate the impact of temporal characteristics of answers in Question Answering (QA) by exploring several simple answer selection techniques. |
Mehmet Kardan; Bhawna Piryani; Adam Jatowt; | arxiv-cs.CL | 2025-03-06 |
| 174 | Question-Aware Gaussian Experts for Audio-Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes QA-TIGER, a novel framework that explicitly incorporates question information and models continuous temporal dynamics. |
HONGYEOB KIM et. al. | arxiv-cs.CV | 2025-03-06 |
| 175 | Dynamic-KGQA: A Scalable Framework for Generating Adaptive Question Answering Datasets Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce Dynamic-KGQA, a scalable framework for generating adaptive QA datasets from knowledge graphs (KGs), designed to mitigate memorization risks while maintaining statistical consistency across iterations. |
Preetam Prabhu Srikar Dammu; Himanshu Naidu; Chirag Shah; | arxiv-cs.CL | 2025-03-06 |
| 176 | Chart-HQA: A Benchmark for Hypothetical Question Answering in Charts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, they overlook the inherent output biases of MLLMs, where models rely on their parametric memory to answer questions rather than genuinely understanding the chart content. To address this limitation, we introduce a novel Chart Hypothetical Question Answering (HQA) task, which imposes assumptions on the same question to compel models to engage in counterfactual reasoning based on the chart content. |
XIANGNAN CHEN et. al. | arxiv-cs.CL | 2025-03-06 |
| 177 | BPQA Dataset: Evaluating How Well Language Models Leverage Blood Pressures to Answer Biomedical Questions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: It is an important component of biomedical data, which can be used to train transformer-based language models (LMs) for improving healthcare delivery. |
CHI HANG et. al. | arxiv-cs.CL | 2025-03-06 |
| 178 | EgoLife: Towards Egocentric Life Assistant Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: By releasing our datasets, models, and benchmarks, we aim to stimulate further research in egocentric AI assistants. |
JINGKANG YANG et. al. | arxiv-cs.CV | 2025-03-05 |
| 179 | DSPNet: Dual-vision Scene Perception for Robust 3D Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a Dual-vision Scene Perception Network (DSPNet), to comprehensively integrate multi-view and point cloud features to improve robustness in 3D QA. |
JINGZHOU LUO et. al. | arxiv-cs.CV | 2025-03-05 |
| 180 | Cross-modal Causal Relation Alignment for Video Question Grounding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel VideoQG framework named Cross-modal Causal Relation Alignment (CRA), to eliminate spurious correlations and improve the causal consistency between question-answering and video temporal grounding. |
WEIXING CHEN et. al. | arxiv-cs.LG | 2025-03-04 |
| 181 | Optimizing Open-domain Question Answering with Graph-based Retrieval Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we benchmark various graph-based retrieval-augmented generation (RAG) systems across a broad spectrum of query types, including OLTP-style (fact-based) and OLAP-style (thematic) queries, to address the complex demands of open-domain question answering (QA). |
JOYCE CAHOON et. al. | arxiv-cs.IR | 2025-03-04 |
| 182 | Towards Robust Expert Finding in Community Question Answering Platforms Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces TUEF, a topic-oriented user-interaction model for fair Expert Finding in Community Question Answering (CQA) platforms. |
Maddalena Amendola; Andrea Passarella; Raffaele Perego; | arxiv-cs.IR | 2025-03-04 |
| 183 | OWLViz: An Open-World Benchmark for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a challenging benchmark for the Open WorLd VISual question answering (OWLViz) task. |
THUY NGUYEN et. al. | arxiv-cs.LG | 2025-03-04 |
| 184 | EchoQA: A Large Collection of Instruction Tuning Data for Echocardiogram Reports Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a novel question-answering (QA) dataset using echocardiogram reports sourced from the Medical Information Mart for Intensive Care database. |
Lama Moukheiber; Mira Moukheiber; Dana Moukheiiber; Jae-Woo Ju; Hyung-Chul Lee; | arxiv-cs.AI | 2025-03-04 |
| 185 | Zero-Shot Complex Question-Answering on Long Scientific Documents Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present a zero-shot pipeline framework that enables social science researchers to perform question-answering tasks that are complex yet of predetermined question formats on full-length research papers without requiring machine learning expertise. |
Wanting Wang; | arxiv-cs.IR | 2025-03-04 |
| 186 | Beyond Prompting: An Efficient Embedding Framework for Open-Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, prevailing retriever-reader pipelines often depend on multiple rounds of prompt level instructions, leading to high computational overhead, instability, and suboptimal retrieval coverage. In this paper, we propose EmbQA, an embedding-level framework that alleviates these shortcomings by enhancing both the retriever and the reader. |
ZHANGHAO HU et. al. | arxiv-cs.CL | 2025-03-03 |
| 187 | SRAG: Structured Retrieval-Augmented Generation for Multi-Entity Question Answering Over Wikipedia Graph Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Multi-entity question answering (MEQA) poses significant challenges for large language models (LLMs), which often struggle to consolidate scattered information across multiple … |
Teng Lin; Yizhang Zhu; Yuyu Luo; Nan Tang; | arxiv-cs.CL | 2025-03-03 |
| 188 | Q-NL Verifier: Leveraging Synthetic Data for Robust Knowledge Graph Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Question answering (QA) requires accurately aligning user questions with structured queries, a process often limited by the scarcity of high-quality query-natural language (Q-NL) pairs. To overcome this, we present Q-NL Verifier, an approach to generating high-quality synthetic pairs of queries and NL translations. |
Tim Schwabe; Louisa Siebel; Patrik Valach; Maribel Acosta; | arxiv-cs.CL | 2025-03-03 |
| 189 | Streaming Video Question-Answering with In-context Video KV-Cache Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose ReKV, a novel training-free approach that enables efficient streaming video question-answering (StreamingVQA), by seamlessly integrating with existing Video Large Language Models (Video-LLMs). |
SHANGZHE DI et. al. | arxiv-cs.CV | 2025-03-01 |
| 190 | CL-MoE: Enhancing Multimodal Large Language Model with Dual Momentum Mixture-of-Experts for Continual Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an MLLMs-based dual momentum Mixture-of-Experts (CL-MoE) framework for continual visual question answering (VQA). |
TIANYU HUAI et. al. | arxiv-cs.CV | 2025-03-01 |
| 191 | AILS-NTUA at SemEval-2025 Task 8: Language-to-Code Prompting and Error Fixing for Tabular Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present our submission to SemEval-2025 Task 8: Question Answering over Tabular Data. |
Andreas Evangelatos; Giorgos Filandrianos; Maria Lymperaiou; Athanasios Voulodimos; Giorgos Stamou; | arxiv-cs.CL | 2025-03-01 |
| 192 | PASemiQA: Plan-Assisted Agent for Question Answering on Semi-Structured Data with Text and Relational Information Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing RAG methods typically focus on a single type of external data, such as vectorized text database or knowledge graphs, and cannot well handle real-world questions on semi-structured data containing both text and relational information. To bridge this gap, we introduce PASemiQA, a novel approach that jointly leverages text and relational information in semi-structured data to answer questions. |
Hansi Yang; Qi Zhang; Wei Jiang; Jianguo Li; | arxiv-cs.CL | 2025-02-28 |
| 193 | WebFAQ: A Multilingual Collection of Natural Q&A Datasets for Dense Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present WebFAQ, a large-scale collection of open-domain question answering datasets derived from FAQ-style schema.org annotations. |
Michael Dinzinger; Laura Caspari; Kanishka Ghosh Dastidar; Jelena Mitrović; Michael Granitzer; | arxiv-cs.CL | 2025-02-28 |
| 194 | Bisecting K-Means in RAG for Enhancing Question-Answering Tasks Performance in Telecommunications Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work presents a novel Retrieval-Augmented Generation framework explicitly designed for the telecommunication domain, focusing on datasets composed of 3GPP documents. |
Pedro Sousa; Cláudio Klautau Mello; Frank B. Morte; Luis F. Solis Navarro; | arxiv-cs.IR | 2025-02-27 |
| 195 | Winning Big with Small Models: Knowledge Distillation Vs. Self-Training for Reducing Hallucination in QA Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The deployment of Large Language Models (LLMs) in customer support is constrained by hallucination-generating false information-and the high cost of proprietary models. To address these challenges, we propose a retrieval-augmented question-answering (QA) pipeline and explore how to balance human input and automation. |
ASHLEY LEWIS et. al. | arxiv-cs.CL | 2025-02-26 |
| 196 | Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, most existing methods and datasets remain focused on a narrow spectrum of tasks, such as forecasting or anomaly detection. To bridge this gap, we introduce Time Series Multi-Task Question Answering (Time-MQA), a unified framework that enables natural language queries across multiple time series tasks – numerical analytical tasks and open-ended question answering with reasoning. |
YAXUAN KONG et. al. | arxiv-cs.CL | 2025-02-26 |
| 197 | M-LLM Based Video Frame Selection for Efficient Video Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, it could lose crucial context in certain periods of a video, so that the downstream M-LLM may not have sufficient visual information to answer a question. To attack this pain point, we propose a light-weight M-LLM -based frame selection method that adaptively select frames that are more relevant to users’ queries. |
KAI HU et. al. | arxiv-cs.CV | 2025-02-26 |
| 198 | MEBench: Benchmarking Large Language Models for Cross-Document Multi-Entity Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: , which require integrating entity-centric insights from heterogeneous sources (e.g., Wikipedia pages). To address this gap, we introduce MEBench, a novel multi-document, multi-entity benchmark designed to systematically evaluate LLMs’ capacity to retrieve, consolidate, and reason over fragmented information. |
Teng Lin; | arxiv-cs.CL | 2025-02-26 |
| 199 | Exploring Rewriting Approaches for Different Conversational Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we systematically investigate two different approaches, denoted as rewriting and fusion, on two fundamentally different generation tasks, including a text-to-text generation task and a multimodal generative task that takes as input text and generates a visualization or data table that answers the user’s question. |
MD MEHRAB TANJIM et. al. | arxiv-cs.CL | 2025-02-26 |
| 200 | Few-Shot Multilingual Open-Domain QA from 5 Examples Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce a \emph{few-shot learning} approach to synthesise large-scale multilingual data from large language models (LLMs). |
Fan Jiang; Tom Drummond; Trevor Cohn; | arxiv-cs.CL | 2025-02-26 |
| 201 | KiRAG: Knowledge-Driven Iterative Retriever for Enhancing Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, their retrieval process faces two key challenges: (1) it can be disrupted by irrelevant documents or factually inaccurate chain-of-thoughts; (2) their retrievers are not designed to dynamically adapt to the evolving information needs in multi-step reasoning, making it difficult to identify and retrieve the missing information required at each iterative step. Therefore, we propose KiRAG, which uses a knowledge-driven iterative retriever model to enhance the retrieval process of iRAG. |
Jinyuan Fang; Zaiqiao Meng; Craig Macdonald; | arxiv-cs.CL | 2025-02-25 |
| 202 | FilterRAG: Zero-Shot Informed Retrieval-Augmented Generation to Mitigate Hallucinations in VQA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce FilterRAG, a retrieval-augmented framework that combines BLIP-VQA with Retrieval-Augmented Generation to ground answers in external knowledge sources like Wikipedia and DBpedia. |
S M Sarwar; | arxiv-cs.CV | 2025-02-25 |
| 203 | Uncertainty Quantification in Retrieval Augmented Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose to quantify the uncertainty of a QA model via estimating the utility of the passages it is provided with. |
Laura Perez-Beltrachini; Mirella Lapata; | arxiv-cs.CL | 2025-02-25 |
| 204 | What Are Foundation Models Cooking in The Post-Soviet World? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we investigate the Post-Soviet cultural food knowledge of foundation models by constructing BORSch, a multimodal dataset encompassing 1147 and 823 dishes in the Russian and Ukrainian languages, centered around the Post-Soviet region. |
Anton Lavrouk; Tarek Naous; Alan Ritter; Wei Xu; | arxiv-cs.CL | 2025-02-25 |
| 205 | Say Less, Mean More: Leveraging Pragmatics in Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a simple, unsupervised method that injects pragmatic principles in retrieval-augmented generation (RAG) frameworks such as Dense Passage Retrieval to enhance the utility of retrieved contexts. |
Haris Riaz; Ellen Riloff; Mihai Surdeanu; | arxiv-cs.CL | 2025-02-24 |
| 206 | MULTITAT: Benchmarking Multilingual Table-and-Text Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address the limitations, we propose the first multilingual TATQA dataset (MULTITAT). |
Xuanliang Zhang; Dingzirui Wang; Keyan Xu; Qingfu Zhu; Wanxiang Che; | arxiv-cs.CL | 2025-02-24 |
| 207 | MultiOCR-QA: Dataset for Evaluating Robustness of LLMs in Question Answering on Multilingual OCR Texts Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce a multilingual QA dataset MultiOCR-QA, designed to analyze the effects of OCR noise on QA systems’ performance. |
Bhawna Piryani; Jamshid Mozafari; Abdelrahman Abdallah; Antoine Doucet; Adam Jatowt; | arxiv-cs.CL | 2025-02-23 |
| 208 | Wrong Answers Can Also Be Useful: PlausibleQA — A Large-Scale QA Dataset with Answer Plausibility Scores Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing QA datasets primarily focus on correct answers without explicit consideration of the plausibility of other candidate answers, limiting opportunity for more nuanced evaluations of models. To address this gap, we introduce PlausibleQA, a large-scale dataset comprising 10,000 questions and 100,000 candidate answers, each annotated with plausibility scores and justifications for their selection. |
Jamshid Mozafari; Abdelrahman Abdallah; Bhawna Piryani; Adam Jatowt; | arxiv-cs.CL | 2025-02-22 |
| 209 | EPERM: An Evidence Path Enhanced Reasoning Model for Knowledge Graph Question and Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, this paper reformulates the KGQA problem as a graphical model and proposes a three-stage framework named the Evidence Path Enhanced Reasoning Model (EPERM) for KGQA. |
Xiao Long; Liansheng Zhuang; Aodi Li; Minghong Yao; Shafei Wang; | arxiv-cs.CL | 2025-02-22 |
| 210 | MHQA: A Diverse, Knowledge Intensive Mental Health Question Answering Challenge for Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our work presents a novel multiple choice dataset, MHQA (Mental Health Question Answering), for benchmarking Language models (LMs). |
SURAJ RACHA et. al. | arxiv-cs.CL | 2025-02-21 |
| 211 | Benchmarking Multimodal RAG Through A Chart-based Document Question-Answering Generation Framework Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing benchmarks primarily focus on simple image-text interactions, overlooking complex visual formats like charts that are prevalent in real-world applications. In this work, we introduce a novel task, Chart-based MRAG, to address this limitation. |
YUMING YANG et. al. | arxiv-cs.AI | 2025-02-20 |
| 212 | Argument-Based Comparative Question Answering Evaluation Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we aim to solve the problems standing in the way of automatic comparative question answering. |
IRINA NIKISHINA et. al. | arxiv-cs.CL | 2025-02-20 |
| 213 | Exploring Advanced Techniques for Visual Question Answering: A Comprehensive Comparison Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Analyzing VQA datasets is essential for developing robust models that can handle the complexities of multimodal reasoning. |
Aiswarya Baby; Tintu Thankom Koshy; | arxiv-cs.CV | 2025-02-20 |
| 214 | PRIV-QA: Privacy-Preserving Question Answering for Cloud Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a privacy preservation pipeline for protecting privacy and sensitive information during interactions between users and LLMs in practical LLM usage scenarios. |
GUANGWEI LI et. al. | arxiv-cs.CL | 2025-02-19 |
| 215 | Quantifying Memorization and Retriever Performance in Retrieval-Augmented Vision-Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we analyze the extent to which multimodal retrieval-augmented VLMs memorize training data compared to baseline VLMs. |
Peter Carragher; Abhinand Jha; R Raghav; Kathleen M. Carley; | arxiv-cs.LG | 2025-02-19 |
| 216 | Mitigating Lost-in-Retrieval Problems in Retrieval Augmented Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we identify a critical problem, lost-in-retrieval, in retrieval-augmented multi-hop question answering (QA): the key entities are missed in LLMs’ sub-question decomposition. |
Rongzhi Zhu; Xiangyu Liu; Zequn Sun; Yiwei Wang; Wei Hu; | arxiv-cs.CL | 2025-02-19 |
| 217 | MuDAF: Long-Context Multi-Document Attention Focusing Through Contrastive Learning on Attention Heads Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose Multi-Document Attention Focusing (MuDAF), a novel method that explicitly optimizes the attention distribution at the head level through contrastive learning. |
WEIHAO LIU et. al. | arxiv-cs.CL | 2025-02-19 |
| 218 | MCTS-KBQA: Monte Carlo Tree Search for Knowledge Base Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although recent approaches leveraging LLMs as agents have demonstrated considerable potential, these studies are inherently constrained by their linear decision-making processes. To address this limitation, we propose a MCTS-based framework that enhances LLMs’ reasoning capabilities through tree search methodology. |
Guanming Xiong; Haochen Li; Wen Zhao; | arxiv-cs.CL | 2025-02-18 |
| 219 | SearchRAG: Can Search Engines Be Helpful for LLM-based Medical Question Answering? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Conventional Retrieval-Augmented Generation (RAG) techniques typically retrieve external information from static knowledge bases, which can be outdated or incomplete, missing fine-grained clinical details essential for accurate medical question answering. In this work, we propose SearchRAG, a novel framework that overcomes these limitations by leveraging real-time search engines. |
YUCHENG SHI et. al. | arxiv-cs.CL | 2025-02-18 |
| 220 | Clinical QA 2.0: Multi-Task Learning for Answer Extraction and Categorization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While transformer-based models such as BERT, BioBERT, and ClinicalBERT have demonstrated state-of-the-art performance in CQA, existing models lack the ability to categorize extracted answers, which is critical for structured retrieval, content filtering, and medical decision support. To address this limitation, we introduce a Multi-Task Learning (MTL) framework that jointly trains CQA models for both answer extraction and medical categorization. |
PRIYARANJAN PATTNAYAK et. al. | arxiv-cs.CL | 2025-02-18 |
| 221 | RGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing retrieval approaches often overlook the importance of factual knowledge, which limits the relevance of retrieved conceptual knowledge and restricts its applicability in real-world scenarios, such as clinical decision-making based on Electronic Health Records (EHRs). This paper introduces RGAR, a recurrence generation-augmented retrieval framework that retrieves both relevant factual and conceptual knowledge from dual sources (i.e., EHRs and the corpus), allowing them to interact and refine each another. |
SICHU LIANG et. al. | arxiv-cs.CL | 2025-02-18 |
| 222 | Multi-Attribute Steering of Language Models Via Targeted Intervention Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing ITI approaches fail to scale to multi-attribute settings with conflicts, such as enhancing helpfulness while also reducing toxicity. To address this, we introduce Multi-Attribute Targeted Steering (MAT-Steer), a novel steering framework designed for selective token-level intervention across multiple attributes. |
Duy Nguyen; Archiki Prasad; Elias Stengel-Eskin; Mohit Bansal; | arxiv-cs.CL | 2025-02-17 |
| 223 | CityEQA: A Hierarchical LLM Agent on Embodied Question Answering Benchmark in City Space Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Embodied Question Answering (EQA) has primarily focused on indoor environments, leaving the complexities of urban settings – spanning environment, action, and perception – largely unexplored. To bridge this gap, we introduce CityEQA, a new task where an embodied agent answers open-vocabulary questions through active exploration in dynamic city spaces. |
YONG ZHAO et. al. | arxiv-cs.AI | 2025-02-17 |
| 224 | LM Agents for Coordinating Multi-User Information Gathering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces PeopleJoin, a benchmark for evaluating LM-mediated collaborative problem solving. |
Harsh Jhamtani; Jacob Andreas; Benjamin Van Durme; | arxiv-cs.CL | 2025-02-17 |
| 225 | Ontology-Guided Reverse Thinking Makes Large Language Models Stronger on Knowledge Graph Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As a result, it is difficult to establish reasoning paths to the purpose, which leads to information loss and redundancy. To address this issue, inspired by human reverse thinking, we propose Ontology-Guided Reverse Thinking (ORT), a novel framework that constructs reasoning paths from purposes back to conditions. |
RUNXUAN LIU et. al. | arxiv-cs.CL | 2025-02-17 |
| 226 | Open-Ended and Knowledge-Intensive Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Through our proposed approach, we achieve a substantial 17.5% improvement in accuracy on multiple choice questions in the KnowIT VQA dataset, establishing new state-of-the-art performance levels. |
Md Zarif Ul Alam; Hamed Zamani; | arxiv-cs.IR | 2025-02-17 |
| 227 | Vendi-RAG: Adaptively Trading-Off Diversity And Quality Significantly Improves Retrieval Augmented Generation With LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces Vendi-RAG, a framework based on an iterative process that jointly optimizes retrieval diversity and answer quality. |
Mohammad Reza Rezaei; Adji Bousso Dieng; | arxiv-cs.CL | 2025-02-16 |
| 228 | The Mirage of Model Editing: Revisiting Evaluation in The Wild Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Despite near-perfect results in artificial evaluations, the effectiveness of model editing in real-world applications remains unexplored. To bridge this gap, we propose to study model editing in question answering (QA) by establishing a rigorous evaluation practice to assess the effectiveness of editing methods in correcting LLMs’ errors. |
WANLI YANG et. al. | arxiv-cs.CL | 2025-02-16 |
| 229 | QuOTE: Question-Oriented Text Embeddings Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present QuOTE (Question-Oriented Text Embeddings), a novel enhancement to retrieval-augmented generation (RAG) systems, aimed at improving document representation for accurate and nuanced retrieval. |
Andrew Neeser; Kaylen Latimer; Aadyant Khatri; Chris Latimer; Naren Ramakrishnan; | arxiv-cs.IR | 2025-02-15 |
| 230 | NitiBench: A Comprehensive Study of LLM Framework Capabilities for Thai Legal Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces NitiBench, a benchmark comprising two datasets: the NitiBench-CCL, covering general Thai financial law, and the NitiBench-Tax, which includes real-world tax law cases requiring advanced legal reasoning. |
PAWITSAPAK AKARAJARADWONG et. al. | arxiv-cs.CL | 2025-02-15 |
| 231 | Evaluating The Meta- and Object-Level Reasoning of Large Language Models for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Large Language Models (LLMs) excel in natural language tasks but still face challenges in Question Answering (QA) tasks requiring complex, multi-step reasoning. We outline the types of reasoning required in some of these tasks, and reframe them in terms of meta-level reasoning (akin to high-level strategic reasoning or planning) and object-level reasoning (embodied in lower-level tasks such as mathematical reasoning). |
Nick Ferguson; Liane Guillou; Alan Bundy; Kwabena Nuamah; | arxiv-cs.CL | 2025-02-14 |
| 232 | Post-training An LLM for RAG? Train on Self-Generated Demonstrations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a recipe for training RAG-enabled LLMs using self-generated demonstrations, thereby avoiding training on out-of-distribution text and integrating retrievals into the LLM responses. |
MATTHEW FINLAYSON et. al. | arxiv-cs.CL | 2025-02-14 |
| 233 | Abduction of Domain Relationships from Data for VQA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study the problem of visual question answering (VQA) where the image and query are represented by ASP programs that lack domain data. |
Al Mehdi Saadat Chowdhury; Paulo Shakarian; Gerardo I. Simari; | arxiv-cs.LO | 2025-02-13 |
| 234 | LP-LM: No Hallucinations in Question Answering with Logic Programming Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces LP-LM, a system that grounds answers to questions in known facts contained in a knowledge base (KB), facilitated through semantic parsing in Prolog, and always produces answers that are reliable. |
Katherine Wu; Yanhong A. Liu; | arxiv-cs.AI | 2025-02-13 |
| 235 | SQuARE: Sequential Question Answering Reasoning Engine for Enhanced Chain-of-Thought in Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces SQuARE (Sequential Question Answering Reasoning Engine), a novel prompting technique designed to improve reasoning through a self-interrogation paradigm. |
Daniel Fleischer; Moshe Berchansky; Gad Markovits; Moshe Wasserblat; | arxiv-cs.CL | 2025-02-13 |
| 236 | Neuro-Conceptual Artificial Intelligence: Integrating OPM with Deep Learning to Enhance Question Answering Quality Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce Neuro-Conceptual Artificial Intelligence (NCAI), a specialization of the neuro-symbolic AI approach that integrates conceptual modeling using Object-Process Methodology (OPM) ISO 19450:2024 with deep learning to enhance question-answering (QA) quality. |
Xin Kang; Veronika Shteingardt; Yuhan Wang; Dov Dori; | arxiv-cs.CL | 2025-02-12 |
| 237 | FoQA: A Faroese Question-Answering Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present FoQA, a Faroese extractive question-answering (QA) dataset with 2,000 samples, created using a semi-automated approach combining Large Language Models (LLMs) and human validation. |
Annika Simonsen; Dan Saattrup Nielsen; Hafsteinn Einarsson; | arxiv-cs.CL | 2025-02-11 |
| 238 | EgoTextVQA: Towards Egocentric Scene-Text Aware Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce EgoTextVQA, a novel and rigorously constructed benchmark for egocentric QA assistance involving scene text. |
SHENG ZHOU et. al. | arxiv-cs.CV | 2025-02-11 |
| 239 | On Mechanistic Circuits for Extractive Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Large language models are increasingly used to process documents and facilitate question-answering on them. |
SAMYADEEP BASU et. al. | arxiv-cs.CL | 2025-02-11 |
| 240 | Intelligent Legal Assistant: An Interactive Clarification System for Legal Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we develop a legal question-answering system called Intelligent Legal Assistant, which interacts with users to precisely capture their needs. |
RUJING YAO et. al. | arxiv-cs.CL | 2025-02-11 |
| 241 | Multi-granular Training Strategies for Robust Multi-hop Reasoning Over Noisy and Heterogeneous Knowledge Sources Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Adaptive Multi-source Knowledge-Oriented Reasoning (AMKOR), a generative framework that leverages large language models (LLMs) to dynamically fuse parametric and retrieved knowledge while exploring reasoning trajectories using probabilistic beam reasoning. |
Jackson Coleman; Isaiah Lawrence; Benjamin Turner; | arxiv-cs.CL | 2025-02-09 |
| 242 | ClinKD: Cross-Modal Clinical Knowledge Distiller For Multi-Task Medical Images Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: These limitations manifest through erroneous spatial localization and misinterpretation of medical images, which primarily arise from two fundamental issues: inadequate image-text alignment and insufficient medical knowledge in general-purpose MLLMs for specialized medical applications. To address these issues, we introduce the Cross-Modal Clinical Knowledge Distiller (ClinKD), an innovative framework designed to enhance image-text alignment and establish more effective medical knowledge adaptation mechanisms, which enables MLLMs to adapt to medical knowledge. |
HONGYU GE et. al. | arxiv-cs.CV | 2025-02-09 |
| 243 | The Role of Prosody in Spoken Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we investigate the role of prosody in Spoken Question Answering. |
Jie Chi; Maureen de Seyssel; Natalie Schluter; | arxiv-cs.CL | 2025-02-07 |
| 244 | ARR: Question Answering with Large Language Models Via Analyzing, Retrieving, and Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces ARR, an intuitive, effective, and general QA solving method that explicitly incorporates three key steps: analyzing the intent of the question, retrieving relevant information, and reasoning step by step. |
Yuwei Yin; Giuseppe Carenini; | arxiv-cs.CL | 2025-02-07 |
| 245 | LLMs to Support A Domain Specific Knowledge Assistant Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work presents a custom approach to developing a domain specific knowledge assistant for sustainability reporting using the International Financial Reporting Standards (IFRS). |
Maria-Flavia Lovin; | arxiv-cs.CL | 2025-02-06 |
| 246 | Understanding and Supporting Formal Email Exchange By Answering AI-Generated Questions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Although systems with Large Language Models (LLMs) were designed to simplify the email replying process, users still need to provide detailed prompts to obtain the expected output. Therefore, we proposed and evaluated an LLM-powered question-and-answer (QA)-based approach for users to reply to emails by answering a set of simple and short questions generated from the incoming email. |
YUSUKE MIURA et. al. | arxiv-cs.HC | 2025-02-06 |
| 247 | PixFoundation: Are We Heading in The Right Direction with Pixel-level Vision Foundation Models? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Surprisingly, some of these methods even downgrade the grounding ability of MLLMs that were never trained with such supervision. In this work, we propose two novel challenging benchmarks and show that MLLMs without pixel-level grounding supervision can outperform the state of the art in such tasks when evaluating both the pixel-level grounding and visual question answering. |
Mennatullah Siam; | arxiv-cs.CV | 2025-02-06 |
| 248 | No Images, No Problem: Retaining Knowledge in Continual VQA with Questions-Only Memory Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce QUestion-only replay with Attention Distillation (QUAD), a novel approach for VQACL that leverages only past task questions for regularisation, eliminating the need to store visual data and addressing both memory and privacy concerns. |
Imad Eddine Marouf; Enzo Tartaglione; Stephane Lathuiliere; Joost van de Weijer; | arxiv-cs.CV | 2025-02-06 |
| 249 | TerraQ: Spatiotemporal Question-Answering on Satellite Image Archives Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: TerraQ is a spatiotemporal question-answering engine for satellite image archives. It is a natural language processing system that is built to process requests for satellite … |
Sergios-Anestis Kefalidis; Konstantinos Plas; Manolis Koubarakis; | arxiv-cs.CV | 2025-02-06 |
| 250 | SMI: An Information-Theoretic Metric for Predicting Model Knowledge Solely from Pre-Training Signals Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we aim to predict performance in closed-book question answering (QA), a vital downstream task indicative of a model’s internal knowledge. |
CHANGHAO JIANG et. al. | arxiv-cs.CL | 2025-02-06 |
| 251 | MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce MedBioLM, a domain-adapted biomedical question-answering model designed to enhance both short-form and long-form queries. |
Seonok Kim; | arxiv-cs.CL | 2025-02-05 |
| 252 | VQA-Levels: A Hierarchical Approach for Classifying Questions in VQA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The work in this paper will go a long way to systematically analyze VQA systems. |
Madhuri Latha Madaka; Chakravarthy Bhagvati; | arxiv-cs.CV | 2025-02-05 |
| 253 | MeDiSumQA: Patient-Oriented Question-Answer Generation from Discharge Letters Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: By releasing MeDiSumQA on PhysioNet, we aim to advance the development of LLMs to enhance patient understanding and ultimately improve care outcomes. |
AMIN DADA et. al. | arxiv-cs.CL | 2025-02-05 |
| 254 | AmaSQuAD: A Benchmark for Amharic Extractive Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This research presents a novel framework for translating extractive question-answering datasets into low-resource languages, as demonstrated by the creation of the AmaSQuAD dataset, a translation of SQuAD 2.0 into Amharic. |
Nebiyou Daniel Hailemariam; Blessed Guda; Tsegazeab Tefferi; | arxiv-cs.CL | 2025-02-04 |
| 255 | TUMTraffic-VideoQA: A Benchmark for Unified Spatio-Temporal Video Understanding in Traffic Scenes Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present TUMTraffic-VideoQA, a novel dataset and benchmark designed for spatio-temporal video understanding in complex roadside traffic scenarios. |
XINGCHENG ZHOU et. al. | arxiv-cs.CV | 2025-02-04 |
| 256 | SensorChat: Answering Qualitative and Quantitative Questions During Long-Term Multimodal Sensor Interactions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce SensorChat, the first end-to-end QA system designed for long-term sensor monitoring with multimodal and high-dimensional data including time series. |
XIAOFAN YU et. al. | arxiv-cs.AI | 2025-02-04 |
| 257 | CondAmbigQA: A Benchmark and Dataset for Conditional Ambiguous Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce Conditional Ambiguous Question-Answering (CondAmbigQA), a benchmark with 200 ambiguous queries and condition-aware evaluation metrics. |
Zongxi Li; Yang Li; Haoran Xie; S. Joe Qin; | arxiv-cs.CL | 2025-02-03 |
| 258 | Language Models Prefer What They Know: Relative Confidence Estimation Via Confidence Preferences Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose relative confidence estimation, where we match up questions against each other and ask the model to make relative judgments of confidence (Which question are you more confident in answering correctly?) |
Vaishnavi Shrivastava; Ananya Kumar; Percy Liang; | arxiv-cs.CL | 2025-02-03 |
| 259 | ChartCitor: Multi-Agent Framework for Fine-Grained Chart Visual Attribution Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present ChartCitor, a multi-agent framework that provides fine-grained bounding box citations by identifying supporting evidence within chart images. |
Kanika Goswami; Puneet Mathur; Ryan Rossi; Franck Dernoncourt; | arxiv-cs.CL | 2025-02-02 |
| 260 | Multilingual State Space Models for Structured Question Answering in Indic Languages Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose enhancements to existing SSM frameworks, optimizing their applicability to low-resource settings and multilingual scenarios prevalent in Indic languages. |
Arpita Vats; Rahul Raja; Mrinal Mathur; Vinija Jain; Aman Chadha; | arxiv-cs.CL | 2025-02-01 |
| 261 | Embodied Intelligence for 3D Understanding: A Survey on 3D Scene Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents the first comprehensive survey of 3D SQA, systematically reviewing datasets, methodologies, and evaluation metrics while highlighting critical challenges and future opportunities in dataset standardization, multimodal fusion, and task design. |
ZECHUAN LI et. al. | arxiv-cs.CV | 2025-02-01 |
| 262 | CALM: Unleashing The Cross-Lingual Self-Aligning Ability of Language Model Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We offer a qualitative analysis of how cross-lingual consistency can enhance knowledge alignment and explore the method’s generalizability. |
Yumeng Wang; Zhiyuan Fan; Qingyun Wang; May Fung; Heng Ji; | arxiv-cs.CL | 2025-01-30 |
| 263 | Cross-Language Approach for Quranic QA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these systems face unique challenges, including the linguistic disparity between questions written in Modern Standard Arabic and answers found in Quranic verses written in Classical Arabic, and the small size of existing datasets, which further restricts model performance. To address these challenges, we adopt a cross-language approach by (1) Dataset Augmentation: expanding and enriching the dataset through machine translation to convert Arabic questions into English, paraphrasing questions to create linguistic diversity, and retrieving answers from an English translation of the Quran to align with multilingual training requirements; and (2) Language Model Fine-Tuning: utilizing pre-trained models such as BERT-Medium, RoBERTa-Base, DeBERTa-v3-Base, ELECTRA-Large, Flan-T5, Bloom, and Falcon to address the specific requirements of Quranic QA. |
Islam Oshallah; Mohamed Basem; Ali Hamdi; Ammar Mohammed; | arxiv-cs.CL | 2025-01-29 |
| 264 | Hybrid Graphs for Table-and-Text Based Question Answering Using LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a novel Hybrid Graph-based approach for Table-Text QA that leverages LLMs without fine-tuning. |
Ankush Agarwal; Ganesh S; Chaitanya Devaguptapu; | arxiv-cs.CL | 2025-01-29 |
| 265 | PISCO: Pretty Simple Compression for Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce PISCO, a novel method that achieves a 16x compression rate with minimal accuracy loss (0-3%) across diverse RAG-based question-answering (QA) tasks. |
Maxime Louis; Hervé Déjean; Stéphane Clinchant; | arxiv-cs.CL | 2025-01-27 |
| 266 | Improving Retrieval-Augmented Generation Through Multi-Agent Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Specifically, we present MMOA-RAG, a Multi-Module joint Optimization Algorithm for RAG, which employs multi-agent reinforcement learning to harmonize all agents’ goals towards a unified reward, such as the F1 score of the final answer. |
YIQUN CHEN et. al. | arxiv-cs.CL | 2025-01-25 |
| 267 | Federated Retrieval Augmented Generation for Multi-Product Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce MKP-QA, a novel multi-product knowledge-augmented QA framework with probabilistic federated search across domains and relevant knowledge. |
PARSHIN SHOJAEE et. al. | arxiv-cs.CL | 2025-01-24 |
| 268 | CG-RAG: Research Question Answering By Citation Graph Retrieval-Augmented LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce Contextualized Graph Retrieval-Augmented Generation (CG-RAG), a novel framework that integrates sparse and dense retrieval signals within graph structures to enhance retrieval efficiency and subsequently improve generation quality for research question answering. |
YUNTONG HU et. al. | arxiv-cs.IR | 2025-01-24 |
| 269 | ReasVQA: Advancing VideoQA with Imperfect Reasoning Process Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce \textbf{ReasVQA} (Reasoning-enhanced Video Question Answering), a novel approach that leverages reasoning processes generated by Multimodal Large Language Models (MLLMs) to improve the performance of VideoQA models. |
JIANXIN LIANG et. al. | arxiv-cs.CV | 2025-01-23 |
| 270 | Question Answering on Patient Medical Records with Private Fine-Tuned LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, ensuring privacy and compliance requires edge and private deployments of LLMs. This paper proposes a novel approach to semantic QA over EHRs by first identifying the most relevant FHIR resources for a user query (Task1) and subsequently answering the query based on these resources (Task2). |
Sara Kothari; Ayush Gupta; | arxiv-cs.CL | 2025-01-23 |
| 271 | ENTER: Event Based Interpretable Reasoning for VideoQA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present ENTER, an interpretable Video Question Answering (VideoQA) system based on event graphs. |
HAMMAD AYYUBI et. al. | arxiv-cs.CV | 2025-01-23 |
| 272 | K-COMP: Retrieval-Augmented Medical Domain Question Answering With Knowledge-Injected Compressor Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: As a result, the documents include some inaccurate information, which could lead the reader model to mistrust the passages and could result in hallucinations. To solve these problems, we propose K-comp (Knowledge-injected compressor) which provides the knowledge required to answer correctly. |
Jeonghun Cho; Gary Geunbae Lee; | arxiv-cs.CL | 2025-01-23 |
| 273 | Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Besides, RAG is not always needed as may introduce irrelevant information. Recent adaptive retrieval methods integrate LLMs’ intrinsic knowledge with external information appealing to LLM self-knowledge, but they often neglect efficiency evaluations and comparisons with uncertainty estimation techniques. |
VIKTOR MOSKVORETSKII et. al. | arxiv-cs.CL | 2025-01-22 |
| 274 | Combining Knowledge Graph and LLMs for Enhanced Zero-shot Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel design to combine knowledge graph and LLMs for zero-shot visual question answer. |
Qian Tao; Xiaoyang Fan; Yong Xu; Xingquan Zhu; Yufei Tang; | arxiv-cs.CV | 2025-01-22 |
| 275 | Question-to-Question Retrieval for Hallucination-Free Knowledge Access: An Approach for Wikipedia and Wikidata Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces an approach to question answering over knowledge bases like Wikipedia and Wikidata by performing question-to-question matching and retrieval from a dense vector embedding store. |
Santhosh Thottingal; | arxiv-cs.CL | 2025-01-20 |
| 276 | A Collection of Question Answering Datasets for Norwegian Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces a new suite of question answering datasets for Norwegian; NorOpenBookQA, NorCommonSenseQA, NorTruthfulQA, and NRK-Quiz-QA. |
Vladislav Mikhailov; Petter Mæhlum; Victoria Ovedie Chruickshank Langø; Erik Velldal; Lilja Øvrelid; | arxiv-cs.CL | 2025-01-19 |
| 277 | Leveraging Chain of Thought Towards Empathetic Spoken Dialogue Without Corresponding Question-Answering Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In response, we propose a novel approach that circumvents the need for question-answering data, called Listen, Perceive, and Express (LPE). |
JINGRAN XIE et. al. | arxiv-cs.CL | 2025-01-18 |
| 278 | InsQABench: Benchmarking Chinese Insurance Domain Question Answering with Large Language Models Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: The application of large language models (LLMs) has achieved remarkable success in various fields, but their effectiveness in specialized domains like the Chinese insurance … |
JING DING et. al. | arxiv-cs.CL | 2025-01-18 |
| 279 | Algorithm for Semantic Network Generation from Texts of Low Resource Languages Such As Kiswahili Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Processing low-resource languages, such as Kiswahili, using machine learning is difficult due to lack of adequate training data. However, such low-resource languages are still … |
Barack Wamkaya Wanjawa; Lawrence Muchemi; Evans Miriti; | arxiv-cs.CL | 2025-01-16 |
| 280 | Passage Segmentation of Documents for Extractive Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study emphasizes the critical role of chunking in improving the performance of both dense passage retrieval and the end-to-end RAG pipeline. |
Zuhong Liu; Charles-Elie Simon; Fabien Caspani; | arxiv-cs.CL | 2025-01-16 |
| 281 | Admitting Ignorance Helps The Video Question Answering Models to Answer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We argue that these models often establish shortcuts, resulting in spurious correlations between questions and answers, especially when the alignment between video and text data is suboptimal. To address these spurious correlations, we propose a novel training framework in which the model is compelled to acknowledge its ignorance when presented with an intervened question, rather than making guesses solely based on superficial question-answer correlations. |
Haopeng Li; Tom Drummond; Mingming Gong; Mohammed Bennamoun; Qiuhong Ke; | arxiv-cs.CV | 2025-01-15 |
| 282 | To Retrieve or Not to Retrieve? Uncertainty Detection for Dynamic Retrieval Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: by exploring multiple uncertainty detection methods. We evaluate these methods for the task of long-form question answering, employing dynamic retrieval, and present our comparisons. |
Kaustubh D. Dhole; | arxiv-cs.CL | 2025-01-15 |
| 283 | ASTRID — An Automated and Scalable TRIaD for The Evaluation of RAG-based Clinical Question Answering Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Using clinical human evaluations of responses is expensive, unscalable, and not conducive to the continuous iterative development of RAG systems. To address these challenges, we introduce ASTRID – an Automated and Scalable TRIaD for evaluating clinical QA systems leveraging RAG – consisting of three metrics: Context Relevance (CR), Refusal Accuracy (RA), and Conversational Faithfulness (CF). |
Mohita Chowdhury; Yajie Vera He; Aisling Higham; Ernest Lim; | arxiv-cs.CL | 2025-01-14 |
| 284 | TimeLogic: A Temporal Logic Benchmark for Video QA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the advancement of vision-language models, assessing their temporal logical reasoning powers remains a challenge, primarily due to the lack QA pairs that demand formal, complex temporal reasoning. To bridge this gap, we introduce the TimeLogic QA (TLQA) framework to automatically generate the QA pairs, specifically designed to evaluate the temporal logical understanding. |
Sirnam Swetha; Hilde Kuehne; Mubarak Shah; | arxiv-cs.CV | 2025-01-13 |
| 285 | Fine-tuning Large Language Models for Improving Factuality in Legal Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We conduct extensive real-data experiments to validate the effectiveness of our approach. |
Yinghao Hu; Leilei Gan; Wenyi Xiao; Kun Kuang; Fei Wu; | arxiv-cs.CL | 2025-01-11 |
| 286 | Finnish SQuAD: A Simple Approach to Machine Translation of Span Annotations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We apply a simple method to machine translate datasets with span-level annotation using the DeepL MT service and its ability to translate formatted documents. |
Emil Nuutinen; Iiro Rastas; Filip Ginter; | arxiv-cs.CL | 2025-01-10 |
| 287 | SensorQA: A Question Answering Benchmark for Daily-Life Monitoring Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While existing research primarily focuses on learning classification models, fewer studies have explored how end users can actively extract useful insights from sensor data, often hindered by the lack of a proper dataset. To address this gap, we introduce SensorQA, the first human-created question-answering (QA) dataset for long-term time-series sensor data for daily life monitoring. |
BENJAMIN REICHMAN et. al. | arxiv-cs.CL | 2025-01-09 |
| 288 | Commonsense Video Question Answering Through Video-Grounded Entailment Tree Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes the first video-grounded entailment tree reasoning method for commonsense video question answering (VQA). |
Huabin Liu; Filip Ilievski; Cees G. M. Snoek; | arxiv-cs.CV | 2025-01-09 |
| 289 | Graph-Based Multimodal Contrastive Learning for Chart Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work introduces a novel joint multimodal scene graph framework that explicitly models the relationships among chart components and their underlying structures. |
Yue Dai; Soyeon Caren Han; Wei Liu; | arxiv-cs.CL | 2025-01-08 |
| 290 | TimelineKGQA: A Comprehensive Question-Answer Pair Generator for Temporal Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a novel categorization framework based on timeline-context relationships, along with \textbf{TimelineKGQA}, a universal temporal QA generator applicable to any TKGs. |
Qiang Sun; Sirui Li; Du Huynh; Mark Reynolds; Wei Liu; | arxiv-cs.LO | 2025-01-08 |
| 291 | Multimodal Multihop Source Retrieval for Web Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate the importance of graph structure for multi-modal multi-hop question answering. |
Navya Yarrabelly; Saloni Mittal; | arxiv-cs.CL | 2025-01-07 |
| 292 | Multilingual Open QA on The MIA Shared Task Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: \par We propose a simple and effective re-ranking method for improving passage retrieval in open question answering. |
Navya Yarrabelly; Saloni Mittal; Ketan Todi; Kimihiro Hasegawa; | arxiv-cs.CL | 2025-01-07 |
| 293 | BoundingDocs: A Unified Dataset for Document Question Answering with Spatial Annotations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a unified dataset for document Question-Answering (QA), which is obtained combining several public datasets related to Document AI and visually rich document understanding (VRDU). |
Simone Giovannini; Fabio Coppini; Andrea Gemelli; Simone Marinai; | arxiv-cs.CL | 2025-01-06 |
| 294 | CoReQA: Uncovering Potentials of Language Models in Code Repository Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These benchmarks fail to capture the real-world complexity of software engineering and user requirements for understanding code repositories. To address this gap, we introduce CoReQA, a benchmark for Code Repository-level question answering, constructed from GitHub issues and comments from 176 popular repositories across four programming languages. |
JIALIANG CHEN et. al. | arxiv-cs.SE | 2025-01-06 |
| 295 | QuIM-RAG: Advancing Retrieval-Augmented Generation with Inverted Question Matching for Enhanced QA Performance Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce QuIM-RAG (Question-to-question Inverted Index Matching), a novel approach for the retrieval mechanism in our system. |
Binita Saha; Utsha Saha; Muhammad Zubair Malik; | arxiv-cs.CL | 2025-01-05 |
| 296 | Survey on Question Answering Over Visually Rich Documents: Methods, Challenges, and Trends Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we provide a comprehensive overview of state-of-the-art approaches, emphasizing their strengths and limitations, pointing out the main challenges in the field, and proposing promising research directions. |
Camille Barboule; Benjamin Piwowarski; Yoan Chabot; | arxiv-cs.CL | 2025-01-04 |
| 297 | Accounting for Focus Ambiguity in Visual Questions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: No existing work on visual question answering explicitly accounts for ambiguity regarding where the content described in the question is located in the image. To fill this gap, we introduce VQ-FocusAmbiguity, the first VQA dataset that visually grounds each region described in the question that is necessary to arrive at the answer. |
Chongyan Chen; Yu-Yun Tseng; Zhuoheng Li; Anush Venkatesh; Danna Gurari; | arxiv-cs.CV | 2025-01-04 |
| 298 | QuArch: A Question-Answering Dataset for AI Agents in Computer Architecture Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce QuArch, a dataset of 1500 human-validated question-answer pairs designed to evaluate and enhance language models’ understanding of computer architecture. |
SHVETANK PRAKASH et. al. | arxiv-cs.AR | 2025-01-03 |
| 299 | HLV-1K: A Large-scale Hour-Long Video Benchmark for Time-Specific Long Video Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Among them, in this paper, we focus on building a large-scale hour-long long video benchmark, HLV-1K, designed to evaluate long video understanding models. |
HEQING ZOU et. al. | arxiv-cs.CV | 2025-01-03 |
| 300 | MoColl: Agent-Based Specific and General Model Collaboration for Image Captioning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing approaches exhibit inherent limitations: specialized models excel in capturing domain-specific details but lack generalization, while vision-language models (VLMs) built on large language models (LLMs) leverage general knowledge but struggle with domain-specific adaptation. To address these limitations, this paper proposes a novel agent-enhanced model collaboration framework, which we call MoColl, designed to effectively integrate domain-specific and general knowledge. |
Pu Yang; Bin Dong; | arxiv-cs.CV | 2025-01-03 |
| 301 | Citations and Trust in LLM Generated Responses Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We explored trust through an anti-monitoring framework, where trust is predicted to be correlated with presence of citations and inversely related to checking citations. |
YIFAN DING et. al. | arxiv-cs.CL | 2025-01-02 |
| 302 | CLIP-UP: CLIP-Based Unanswerable Problem Detection for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Still, these models can make distinctly unnatural errors, for example, providing (wrong) answers to unanswerable VQA questions, such as questions asking about objects that do not appear in the image. To address this issue, we propose CLIP-UP: CLIP-based Unanswerable Problem detection, a novel lightweight method for equipping VLMs with the ability to withhold answers to unanswerable questions. |
Ben Vardi; Oron Nir; Ariel Shamir; | arxiv-cs.CV | 2025-01-02 |
| 303 | Advancing Singlish Understanding: Bridging The Gap with Datasets and Multimodal Models Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Singlish, a Creole language rooted in English, is a key focus in linguistic research within multilingual and multicultural contexts. However, its spoken form remains … |
BIN WANG et. al. | arxiv-cs.CL | 2025-01-01 |
| 304 | LLM-MedQA: Enhancing Medical Question Answering Through Case Studies in Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These limitations undermine their effectiveness in critical medical applications. To address these issues, we propose a novel approach incorporating similar case generation within a multi-agent medical question-answering (MedQA) system. |
HANG YANG et. al. | arxiv-cs.CL | 2024-12-31 |
| 305 | MapQaTor: A System for Efficient Annotation of Map Query Datasets Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce MapQaTor, a web application that streamlines the creation of reproducible, traceable map-based QA datasets. |
Mahir Labib Dihan; Mohammed Eunus Ali; Md Rizwan Parvez; | arxiv-cs.CL | 2024-12-30 |
| 306 | An Empirical Evaluation of Large Language Models on Consumer Health Questions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study evaluates the performance of several Large Language Models (LLMs) on MedRedQA, a dataset of consumer-based medical questions and answers by verified experts extracted from the AskDocs subreddit. |
Moaiz Abrar; Yusuf Sermet; Ibrahim Demir; | arxiv-cs.CL | 2024-12-30 |
| 307 | Audiopedia: Audio QA with Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce Audiopedia, a novel task called Audio Question Answering with Knowledge, which requires both audio comprehension and external knowledge reasoning. |
Abhirama Subramanyam Penamakuri; Kiran Chhatre; Akshat Jain; | arxiv-cs.LG | 2024-12-29 |
| 308 | Building A Rich Dataset to Empower The Persian Question Answering Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, a comprehensive open-domain dataset is presented for Persian. |
Mohsen Yazdinejad; Marjan Kaedi; | arxiv-cs.CL | 2024-12-28 |
| 309 | Pre-training, Fine-tuning and Re-ranking: A Three-Stage Framework for Legal Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a three-stage (\underline{p}re-training, \underline{f}ine-tuning and \underline{r}e-ranking) framework for \underline{l}egal \underline{QA} (called PFR-LQA), which promotes the fine-grained text representation learning and boosts the performance of dense retrieval with the dual-encoder architecture. |
Shiwen Ni; Hao Cheng; Min Yang; | arxiv-cs.CL | 2024-12-27 |
| 310 | Perceive, Query & Reason: Enhancing Video QA with Question-Guided Temporal Queries Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we investigate diverse temporal modeling techniques to integrate with MLLMs, aiming to achieve question-guided temporal modeling that leverages pre-trained visual and textual alignment in MLLMs. |
ROBERTO AMOROSO et. al. | arxiv-cs.CV | 2024-12-26 |
| 311 | Unlocking The Potential of Multiple BERT Models for Bangla Question Answering in NCTB Textbooks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study investigates the capability of state-of-the-art language models-RoBERTa Base, Bangla-BERT, and BERT Base-in automatically assessing Bangla passage-based question-answering from the National Curriculum and Textbook Board (NCTB) textbooks for classes 6-10. |
Abdullah Khondoker; Enam Ahmed Taufik; Md Iftekhar Islam Tashik; S M Ishtiak mahmud; Antara Firoz Parsa; | arxiv-cs.CL | 2024-12-24 |
| 312 | HAUR: Human Annotation Understanding and Recognition Through Text-Heavy Images Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these models exhibit significant limitations in understanding human annotations on text-heavy images. To address this, we propose the Human Annotation Understanding and Recognition (HAUR) task. |
Yuchen Yang; Haoran Yan; Yanhao Chen; Qingqiang Wu; Qingqi Hong; | arxiv-cs.CV | 2024-12-24 |
| 313 | Multi-Agents Based on Large Language Models for Knowledge-based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In addition, humans also tend to collaborate and discuss with others to get better answers. Inspired by this, we propose the multi-agent voting framework. |
Zhongjian Hu; Peng Yang; Bing Li; Zhenqi Wang; | arxiv-cs.CL | 2024-12-24 |
| 314 | VidCtx: Context-aware Video Question Answering with Image Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address those shortcomings, in this paper, we introduce VidCtx, a novel training-free VideoQA framework which integrates both modalities, i.e. both visual information from input frames and textual descriptions of others frames that give the appropriate context. |
Andreas Goulas; Vasileios Mezaris; Ioannis Patras; | arxiv-cs.CV | 2024-12-23 |
| 315 | Factuality or Fiction? Benchmarking Modern LLMs on Ambiguous QA with Citations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we evaluate the factual accuracy and citation performance of state-of-the-art LLMs on the task of Question Answering (QA) in ambiguous settings with source citations. |
Maya Patel; Aditi Anand; | arxiv-cs.CL | 2024-12-23 |
| 316 | Prompting Large Language Models with Rationale Heuristics for Knowledge-based Visual Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a framework called PLRH that Prompts LLMs with Rationale Heuristics for knowledge-based VQA. |
Zhongjian Hu; Peng Yang; Bing Li; Fengyuan Liu; | arxiv-cs.CL | 2024-12-22 |
| 317 | MINTQA: A Multi-Hop Question Answering Benchmark for Evaluating LLMs on New and Tail Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing benchmarks often fail to fully address these challenges. To bridge this gap, we introduce MINTQA (Multi-hop Question Answering on New and Tail Knowledge), a comprehensive benchmark to evaluate LLMs’ capabilities in multi-hop reasoning across four critical dimensions: question handling strategy, sub-question generation, retrieval-augmented generation, and iterative or dynamic decomposition and retrieval. |
Jie He; Nan Hu; Wanqiu Long; Jiaoyan Chen; Jeff Z. Pan; | arxiv-cs.CL | 2024-12-22 |
| 318 | SilVar: Speech Driven Multimodal Model for Reasoning Visual Question Answering and Object Localization Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Moreover, the quality of language models depends on reasoning and prompting techniques, such as COT, which remain underexplored when using speech instructions. To address these challenges, we propose SilVar, a novel end-to-end multimodal model that uses speech instructions for reasoning in visual question answering. |
Tan-Hanh Pham; Hoang-Nam Le; Phu-Vinh Nguyen; Chris Ngo; Truong-Son Hy; | arxiv-cs.CV | 2024-12-21 |
| 319 | DragonVerseQA: Open-Domain Long-Form Context-Aware Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes a novel approach to develop an open-domain and long-form Over-The-Top (OTT) Question-Answering (QA) dataset, DragonVerseQA, specifically oriented to the fantasy universe of House of the Dragon and Game Of Thrones TV series. |
Aritra Kumar Lahiri; Qinmin Vivian Hu; | arxiv-cs.CL | 2024-12-21 |
| 320 | Automated CVE Analysis: Harnessing Machine Learning In Designing Question-Answering Models For Cybersecurity Information Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To tackle these challenges, it is necessary to develop a cybersecurity-specific dataset and train a machine learning model on it, aimed at enhancing the understanding and retrieval of domain-specific information. This paper presents a novel dataset and describes a machine learning model trained on this dataset for the QA task. |
Tanjim Bin Faruk; | arxiv-cs.CR | 2024-12-20 |
| 321 | MRAG: A Modular Retrieval Framework for Time-Sensitive Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To systematically study time-sensitive question answering, we introduce the TempRAGEval benchmark, which repurposes existing datasets by incorporating temporal perturbations and gold evidence labels. |
ZHANG SIYUE et. al. | arxiv-cs.CL | 2024-12-19 |
| 322 | PolySmart @ TRECVid 2024 Medical Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we use text-to-text retrieval to find relevant videos for a medical question based on the similarity of video transcript and answers generated by GPT4. |
Jiaxin Wu; Yiyang Jiang; Xiao-Yong Wei; Qing Li; | arxiv-cs.CV | 2024-12-19 |
| 323 | NGQA: A Nutritional Graph Question Answering Benchmark for Personalized Health-aware Nutritional Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: On the other hand, while large language models (LLMs), a popular solution for this task, demonstrate strong reasoning abilities, they struggle with the domain-specific complexities of personalized healthy dietary reasoning, and existing benchmarks fail to capture these challenges. To address these gaps, we introduce the Nutritional Graph Question Answering (NGQA) benchmark, the first graph question answering dataset designed for personalized nutritional health reasoning. |
ZHEYUAN ZHANG et. al. | arxiv-cs.CL | 2024-12-19 |
| 324 | Review-Then-Refine: A Dynamic Framework for Multi-Hop Question Answering with Temporal Adaptability Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the challenge, this paper proposes a novel framework called review-then-refine, which aims to enhance LLM performance in multi-hop QA scenarios with temporal information. |
Xiangsen Chen; Xuming Hu; Nan Tang; | arxiv-cs.CL | 2024-12-19 |
| 325 | Multimodal Hypothetical Summary for Retrieval-based Multi-image Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Conventional retrieve-then-answer pipelines often suffer from cascading errors because the training objective of QA fails to optimize the retrieval stage. To address this issue, we propose a novel method to effectively introduce and reference retrieved information into the QA. |
Peize Li; Qingyi Si; Peng Fu; Zheng Lin; Yan Wang; | arxiv-cs.CV | 2024-12-19 |
| 326 | CodeRepoQA: A Large-scale Benchmark for Software Engineering Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce CodeRepoQA, a large-scale benchmark specifically designed for evaluating repository-level question-answering capabilities in the field of software engineering. |
RUIDA HU et. al. | arxiv-cs.SE | 2024-12-19 |
| 327 | Multi-OphthaLingua: A Multilingual Benchmark for Assessing and Debiasing LLM Ophthalmological QA in LMICs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing debiasing methods such as Translation Chain-of-Thought or Retrieval-augmented generation (RAG) by themselves fall short of closing this performance gap, often failing to improve performance across all languages and lacking specificity for the medical domain. To address this issue, We propose CLARA (Cross-Lingual Reflective Agentic system), a novel inference time de-biasing method leveraging retrieval augmented generation and self-verification. |
DAVID RESTREPO et. al. | arxiv-cs.CL | 2024-12-18 |
| 328 | GraphEQA: Using 3D Semantic Scene Graphs for Real-time Embodied Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This remains a challenging problem in robotics, due to the difficulties in obtaining useful semantic representations, updating these representations online, and leveraging prior world knowledge for efficient exploration and planning. Aiming to address these limitations, we propose GraphEQA, a novel approach that utilizes real-time 3D metric-semantic scene graphs (3DSGs) and task relevant images as multi-modal memory for grounding Vision-Language Models (VLMs) to perform EQA tasks in unseen environments. |
SAUMYA SAXENA et. al. | arxiv-cs.RO | 2024-12-18 |
| 329 | Question: How Do Large Language Models Perform on The Question Answering Tasks? Answer: Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we propose a comprehensive performance comparison between smaller fine-tuned models and out-of-the-box instruction-following LLMs on the Stanford Question Answering Dataset 2.0 (SQuAD2), specifically when using a single-inference prompting technique. |
Kevin Fischer; Darren Fürst; Sebastian Steindl; Jakob Lindner; Ulrich Schäfer; | arxiv-cs.CL | 2024-12-17 |
| 330 | EXIT: Context-Aware Extractive Compression for Enhancing Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce EXIT, an extractive context compression framework that enhances both the effectiveness and efficiency of retrieval-augmented generation (RAG) in question answering (QA). |
TAEHO HWANG et. al. | arxiv-cs.CL | 2024-12-17 |
| 331 | LLM-based Discriminative Reasoning for Knowledge Graph Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, LLMs often produce ungrounded subgraph planning or reasoning results in KGQA due to the hallucinatory behavior brought by the generative paradigm. To tackle this issue, we propose READS to reformulate the KGQA process into discriminative subtasks, which simplifies the search space for each subtasks. |
MUFAN XU et. al. | arxiv-cs.CL | 2024-12-17 |
| 332 | Interpretable LLM-based Table Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although recent approaches using Large Language Models (LLMs) have significantly improved Table QA performance, their explanations for how the answers are generated are ambiguous. To fill this gap, we introduce Plan-of-SQLs (POS), an interpretable Table QA approach designed to improve users’ understanding of model decision-making. |
GIANG NGUYEN et. al. | arxiv-cs.CL | 2024-12-16 |
| 333 | Context Filtering with Reward Modeling in Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Yet, the mix of relevant and irrelevant information in these contexts can hinder performance enhancements in QA tasks. To address this, we introduce a context filtering approach that removes non-essential details, summarizing crucial content through Reward Modeling. |
Sangryul Kim; James Thorne; | arxiv-cs.CL | 2024-12-16 |
| 334 | SCITAT: A Question Answering Benchmark for Scientific Tables and Text Covering Diverse Reasoning Types Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, current SQA datasets have limited reasoning types and neglect the relevance between tables and text, creating a significant gap with real scenarios. To address these challenges, we propose a QA benchmark for scientific tables and text with diverse reasoning types (SciTaT). |
XUANLIANG ZHANG et. al. | arxiv-cs.CL | 2024-12-16 |
| 335 | CG-Bench: Clue-grounded Question Answering Benchmark for Long Video Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, because of the inherent limitation of MCQ-based evaluation and the increasing reasoning ability of MLLMs, models can give the current answer purely by combining short video understanding with elimination, without genuinely understanding the video content. To address this gap, we introduce CG-Bench, a novel benchmark designed for clue-grounded question answering in long videos. |
GUO CHEN et. al. | arxiv-cs.CV | 2024-12-16 |
| 336 | Precise Length Control in Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a method to adapt pre-trained decoder-only LLMs for precise control of response length. |
Bradley Butcher; Michael O’Keefe; James Titchener; | arxiv-cs.CL | 2024-12-16 |
| 337 | Advancements and Challenges in Bangla Question Answering Models: A Comprehensive Review Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The domain of Natural Language Processing (NLP) has experienced notable progress in the evolution of Bangla Question Answering (QA) systems. This paper presents a comprehensive review of seven research articles that contribute to the progress in this domain. |
Md Iftekhar Islam Tashik; Abdullah Khondoker; Enam Ahmed Taufik; Antara Firoz Parsa; S M Ishtiak Mahmud; | arxiv-cs.CL | 2024-12-16 |
| 338 | Overview of TREC 2024 Medical Video Question Answering (MedVidQA) Track Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: With increasing interest in AI to support clinical decision-making and improve patient engagement, there is a need to explore such challenges and develop efficient algorithms for medical language-video understanding and generation. Toward this, we introduced new tasks to foster research toward designing systems that can understand medical videos to provide visual answers to natural language questions, and are equipped with multimodal capability to generate instruction steps from the medical video. |
Deepak Gupta; Dina Demner-Fushman; | arxiv-cs.CV | 2024-12-15 |
| 339 | Patch-level Sounding Object Tracking for Audio-Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a new Patch-level Sounding Object Tracking (PSOT) method. |
ZHANGBIN LI et. al. | arxiv-cs.MM | 2024-12-14 |
| 340 | Lost in The Middle, and In-Between: Enhancing Language Models’ Ability to Reason Over Long Contexts in Multi-Hop QA Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Here, we demonstrate the effects of the lost in the middle problem in the multi-hop question answering setting — in which multiple reasoning hops over disconnected documents are required — and show that performance degrades not only with respect to the distance of information from the edges of the context, but also between pieces of information. |
George Arthur Baker; Ankush Raut; Sagi Shaier; Lawrence E Hunter; Katharina von der Wense; | arxiv-cs.CL | 2024-12-13 |
| 341 | RETQA: A Large-Scale Open-Domain Tabular Question Answering Dataset for Real Estate Sector Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Compared with existing tabular question answering datasets, RETQA poses greater challenges due to three key factors: long-table structures, open-domain retrieval, and multi-domain queries. To tackle these challenges, we propose the SLUTQA framework, which integrates large language models with spoken language understanding tasks to enhance retrieval and answering accuracy. |
Zhensheng Wang; Wenmian Yang; Kun Zhou; Yiquan Zhang; Weijia Jia; | arxiv-cs.CL | 2024-12-13 |
| 342 | VLR-Bench: Multilingual Benchmark Dataset for Vision-Language Retrieval Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose the VLR-Bench, a visual question answering (VQA) benchmark for evaluating vision language models (VLMs) based on retrieval augmented generation (RAG). |
HYEONSEOK LIM et. al. | arxiv-cs.CV | 2024-12-13 |
| 343 | Evidence Contextualization and Counterfactual Attribution for Conversational QA Over Heterogeneous Data with RAG Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, several RAG systems today suffer from two shortcomings: (i) retrieved passages usually contain their raw text and lack appropriate document context, negatively impacting both retrieval and answering quality; and (ii) attribution strategies that explain answer generation typically rely only on similarity between the answer and the retrieved passages, thereby only generating plausible but not causal explanations. In this work, we demonstrate RAGONITE, a RAG system that remedies the above concerns by: (i) contextualizing evidence with source metadata and surrounding text; and (ii) computing counterfactual attribution, a causal explanation approach where the contribution of an evidence to an answer is determined by the similarity of the original response to the answer obtained by removing that evidence. |
RISHIRAJ SAHA ROY et. al. | arxiv-cs.CL | 2024-12-13 |
| 344 | Foundation Models and Adaptive Feature Selection: A Synergistic Approach to Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce Local-Global Question Aware Video Embedding (LGQAVE), which incorporates three major innovations to integrate multi-modal knowledge better and emphasize semantic visual concepts relevant to specific questions. |
SAI BHARGAV RONGALI et. al. | arxiv-cs.CV | 2024-12-12 |
| 345 | Towards A Multimodal Large Language Model with Pixel-Level Insight for Biomedicine Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce a novel end-to-end multimodal large language model for the biomedical domain, named MedPLIB, which possesses pixel-level understanding. |
XIAOSHUANG HUANG et. al. | arxiv-cs.CV | 2024-12-12 |
| 346 | Assessing The Robustness of Retrieval-Augmented Generation Systems in K-12 Educational Question Answering with Knowledge Discrepancies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the discrepancy between textbooks and the parametric knowledge in Large Language Models (LLMs) could undermine the effectiveness of RAG systems. To systematically investigate the robustness of RAG systems under such knowledge discrepancies, we present EduKDQA, a question answering dataset that simulates knowledge discrepancies in real applications by applying hypothetical knowledge updates in answers and source documents. |
Tianshi Zheng; Weihan Li; Jiaxin Bai; Weiqi Wang; Yangqiu Song; | arxiv-cs.CL | 2024-12-12 |
| 347 | Discrete Subgraph Sampling for Interpretable Graph Based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we integrate different discrete subset sampling methods into a graph-based visual question answering system to compare their effectiveness in generating interpretable explanatory subgraphs intrinsically. |
Pascal Tilli; Ngoc Thang Vu; | arxiv-cs.CL | 2024-12-11 |
| 348 | Piece of Table: A Divide-and-Conquer Approach for Selecting Subtables in Table Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, when applying linearized tables to LMs, the maximum token lengths often imposed in self-attention calculations make it difficult to comprehensively understand the context spread across large tables. To address these challenges, we present PieTa (Piece of Table), a new framework for subtable-based question answering (QA). |
Wonjin Lee; Kyumin Kim; Sungjae Lee; Jihun Lee; Kwang In Kim; | arxiv-cs.CL | 2024-12-10 |
| 349 | Ontology-Aware RAG for Improved Question-Answering in Cybersecurity Education Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Integrating AI into education has the potential to transform the teaching of science and technology courses, particularly in the field of cybersecurity. AI-driven … |
CHENGSHUAI ZHAO et. al. | ArXiv | 2024-12-10 |
| 350 | RAG-based Question Answering Over Heterogeneous Data and Text Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This article presents the QUASAR system for question answering over unstructured text, structured tables, and knowledge graphs, with unified treatment of all sources. |
Philipp Christmann; Gerhard Weikum; | arxiv-cs.CL | 2024-12-10 |
| 351 | AutoPrep: Natural Language Question-Aware Data Preparation with A Multi-Agent Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose AutoPrep, a large language model (LLM)-based multi-agent framework that leverages the strengths of multiple agents, each specialized in a certain type of data prep, ensuring more accurate and contextually relevant responses. |
MEIHAO FAN et. al. | arxiv-cs.CL | 2024-12-10 |
| 352 | Ranked from Within: Ranking Large Multimodal Models for Visual Question Answering Without Labels Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we explore unsupervised model ranking for LMMs by leveraging their uncertainty signals, such as softmax probabilities. |
WEIJIE TU et. al. | arxiv-cs.CV | 2024-12-09 |
| 353 | PediaBench: A Comprehensive Chinese Pediatric Dataset for Benchmarking Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Through an in-depth analysis of experimental results, we offer insights into the ability of LLMs to answer pediatric questions in the Chinese context, highlighting their limitations for further improvements. |
QIAN ZHANG et. al. | arxiv-cs.CL | 2024-12-09 |
| 354 | FM2DS: Few-Shot Multimodal Multihop Data Synthesis with Knowledge Distillation for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing methods focus on single-hop, single-modality, or short texts, limiting real-world applications like interpreting educational documents with long, multimodal content. To fill this gap, we introduce FM2DS, the first framework for creating a high-quality dataset for MMQA. |
Amirhossein Abaskohi; Spandana Gella; Giuseppe Carenini; Issam H. Laradji; | arxiv-cs.CL | 2024-12-09 |
| 355 | An Entailment Tree Generation Approach for Multimodal Multi-Hop Question Answering with Mixture-of-Experts and Iterative Feedback Mechanism Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: 2) The reasoning process without interpretable reasoning steps makes the model difficult to discover the logical errors for handling complex questions. To solve these problems, we propose a unified LLMs-based approach but without heavily relying on them due to the LLM’s potential errors, and innovatively treat multimodal multi-hop question answering as a joint entailment tree generation and question answering problem. |
QING ZHANG et. al. | arxiv-cs.CL | 2024-12-08 |
| 356 | Evaluating Hallucination in Text-to-Image Diffusion Models with Scene-Graph Based Question-Answering Agent Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We believe that an effective T2I evaluation metric should accomplish the following: detect instances where the generated images do not align with the textual prompts, a discrepancy we define as the `hallucination problem’ in T2I tasks; record the types and frequency of hallucination issues, aiding users in understanding the causes of errors; and provide a comprehensive and intuitive scoring that close to human standard. To achieve these objectives, we propose a method based on large language models (LLMs) for conducting question-answering with an extracted scene-graph and created a dataset with human-rated scores for generated images. |
ZIYUAN QIN et. al. | arxiv-cs.CV | 2024-12-07 |
| 357 | Knowledge Graphs Are All You Need: Leveraging KGs in Physics Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a pipeline aimed at enhancing model response quality for Question Answering tasks. |
KRISHNASAI ADDALA et. al. | arxiv-cs.CL | 2024-12-06 |
| 358 | SLA Management in Reconfigurable Multi-Agent RAG: A Systems Approach to Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present a systems-oriented approach to multi-agent RAG tailored for real-world Question Answering (QA) applications. |
Michael Iannelli; Sneha Kuchipudi; Vera Dvorak; | arxiv-cs.SE | 2024-12-06 |
| 359 | SplaXBERT: Leveraging Mixed Precision Training and Context Splitting for Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: SplaXBERT, built on ALBERT-xlarge with context-splitting and mixed precision training, achieves high efficiency in question-answering tasks on lengthy texts. Tested on SQuAD v1.1, … |
Zhu Yufan; Hao Zeyu; Li Siqi; Niu Boqian; | arxiv-cs.CL | 2024-12-06 |
| 360 | GRAF: Graph Retrieval Augmented By Facts for Legal Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We first introduce JuRO, the first openly available Romanian legal MCQA dataset, comprising three different examinations and a number of 10,836 total questions. Along with this dataset, we introduce CROL, an organized corpus of laws that has a total of 93 distinct documents with their modifications from 763 time spans, that we leveraged in this work for Information Retrieval (IR) techniques. |
Cristian-George Crăciun; Răzvan-Alexandru Smădu; Dumitru-Clementin Cercel; Mihaela-Claudia Cercel; | arxiv-cs.CL | 2024-12-05 |
| 361 | Question Answering for Decisionmaking in Green Building Design: A Multimodal Data Reasoning Method Driven By Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on previous research, this study innovatively integrates large language models with DGBD, creating GreenQA, a question answering framework for multimodal data reasoning. |
Yihui Li; Xiaoyue Yan; Hao Zhou; Borong Lin; | arxiv-cs.AI | 2024-12-05 |
| 362 | Prompt Engineering Guidance for Conceptual Agent-based Model Extraction Using Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This document contains detailed information about the prompts used in the experimental process discussed in the paper Toward Automating Agent-based Model Generation: A Benchmark for Model Extraction using Question-Answering Techniques. |
Siamak Khatami; Christopher Frantz; | arxiv-cs.MA | 2024-12-05 |
| 363 | Give Me Some Hard Questions: Synthetic Data Generation for Clinical QA Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We find that naive prompting often results in easy questions that do not reflect the complexity of clinical scenarios. To address this, we propose two prompting strategies: 1) instructing the model to generate questions that do not overlap with the input context, and 2) summarizing the input record using a predefined schema to scaffold question generation. |
FAN BAI et. al. | arxiv-cs.CL | 2024-12-05 |
| 364 | Domain-specific Question Answering with Hybrid Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we show that a hybrid approach combining a fine-tuned dense retriever with keyword based sparse search methods significantly enhances performance. |
DEWANG SULTANIA et. al. | arxiv-cs.CL | 2024-12-04 |
| 365 | Copy-Move Forgery Detection and Question Answering for Remote Sensing Image Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces the task of Remote Sensing Copy-Move Question Answering (RSCMQA). |
ZE ZHANG et. al. | arxiv-cs.CV | 2024-12-03 |
| 366 | QA-TOOLBOX: Conversational Question-Answering for Process Task Guidance in Manufacturing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work we explore utilizing LLMs for data augmentation for manufacturing task guidance system. |
RAMESH MANUVINAKURIKE et. al. | arxiv-cs.CL | 2024-12-03 |
| 367 | Hybrid-SQuAD: Hybrid Scholarly Question Answering Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, scholarly information often spans heterogeneous sources, necessitating the development of QA systems that integrate information from multiple heterogeneous data sources. To address this challenge, we introduce Hybrid-SQuAD (Hybrid Scholarly Question Answering Dataset), a novel large-scale QA dataset designed to facilitate answering questions incorporating both text and KG facts. |
Tilahun Abedissa Taffa; Debayan Banerjee; Yaregal Assabie; Ricardo Usbeck; | arxiv-cs.CL | 2024-12-03 |
| 368 | An Evolutionary Large Language Model for Hallucination Mitigation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose EvoLLMs, an innovative framework inspired by Evolutionary Computation, which automates the generation of high-quality Question-answering (QA) datasets while minimizing hallucinations. |
Abdennour Boulesnane; Abdelhakim Souilah; | arxiv-cs.CL | 2024-12-03 |
| 369 | GraphOTTER: Evolving LLM-based Graph Reasoning for Complex Table Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose GraphOTTER that explicitly establishes the reasoning process to pinpoint the correct answers. |
QIANLONG LI et. al. | arxiv-cs.CL | 2024-12-02 |
| 370 | Eyes on The Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The framework leverages GPT-4o to assess accuracy, relevance, and consistency across basic detection, temporal reasoning, and decomposition queries. |
Joseph Raj Vishal; Divesh Basina; Aarya Choudhary; Bharatesh Chakravarthi; | arxiv-cs.CV | 2024-12-02 |
| 371 | A Lightweight Transformer-based Visual Question Answering Network with Weight-Sharing Hybrid Attention Related Papers Related Patents Related Grants Related Venues Related Experts View |
Yue Zhu; Dongyue Chen; Tong Jia; Shizhuo Deng; | Neurocomputing | 2024-12-01 |
| 372 | GS-CBR-KBQA: Graph-structured Case-based Reasoning for Knowledge Base Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
Jiecheng Li; Xudong Luo; Guangquan Lu; | Expert Syst. Appl. | 2024-12-01 |
| 373 | Different Paths to The Same Destination: Diversifying LLMs Generation for Multi-hop Open-domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
Ronghan Li; Yu Wang; Zijian Wen; Mingze Cui; Qiguang Miao; | Knowl. Based Syst. | 2024-12-01 |
| 374 | Automated Construction Safety Reporting System Integrating Deep Learning-based Real-time Advanced Detection and Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
Shihao Wen; Minsoo Park; D. Tran; Seungsoo Lee; Seunghee Park; | Adv. Eng. Softw. | 2024-12-01 |
| 375 | Generative Language Models Potential for Requirement Engineering Applications: Insights Into Current Strengths and Limitations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Traditional language models have been extensively evaluated for software engineering domain, however the potential of ChatGPT and Gemini have not been fully explored. To fulfill this gap, the paper in hand presents a comprehensive case study to investigate the potential of both language models for development of diverse types of requirement engineering applications. |
Summra Saleem; Muhammad Nabeel Asim; Ludger Van Elst; Andreas Dengel; | arxiv-cs.SE | 2024-12-01 |
| 376 | HintMiner: Automatic Question Hints Mining From Q&A Web Posts with Language Model Via Self-Supervised Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The Question – Answering (QA) forums such as Stack Overflow cannot always respond to the questions timely and properly. In this paper, we propose HintMiner, a novel automatic question hints mining tool for users to help them find answers. |
Zhenyu Zhang; JiuDong Yang; | aistats | 2024-12-01 |
| 377 | DynRank: Improving Passage Retrieval with Dynamic Zero-Shot Prompting Based on Question Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents DynRank, a novel framework for enhancing passage retrieval in open-domain question-answering systems through dynamic zero-shot question classification. |
Abdelrahman Abdallah; Jamshid Mozafari; Bhawna Piryani; Mohammed M. Abdelgwad; Adam Jatowt; | arxiv-cs.CL | 2024-11-30 |
| 378 | Perception Test 2024: Challenge Summary and A Novel Hour-Long VideoQA Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We summarise in this report the challenge tasks and results, and introduce in detail the novel hour-long video QA benchmark 1h-walk VQA. |
Joseph Heyward; João Carreira; Dima Damen; Andrew Zisserman; Viorica Pătrăucean; | arxiv-cs.CV | 2024-11-29 |
| 379 | TQA-Bench: Evaluating LLMs for Multi-Table Question Answering with Scalable Context and Symbolic Extension Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing benchmarks primarily focus on single-table QA, failing to capture the intricacies of reasoning across multiple relational tables, as required in real-world domains such as finance, healthcare, and e-commerce. To address this gap, we present TQA-Bench, a new multi-table QA benchmark designed to evaluate the capabilities of LLMs in tackling complex QA tasks over relational data. |
Zipeng Qiu; You Peng; Guangxin He; Binhang Yuan; Chen Wang; | arxiv-cs.AI | 2024-11-29 |
| 380 | Actions and Objects Pathways for Domain Adaptation in Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce the Actions and Objects Pathways (AOPath) for out-of-domain generalization in video question answering tasks. |
Safaa Abdullahi Moallim Mohamud; Ho-Young Jung; | arxiv-cs.CV | 2024-11-28 |
| 381 | Overview of TREC 2024 Biomedical Generative Retrieval (BioGen) Track Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Methods for grounding generated statements in reliable sources along with practical evaluation approaches are needed to overcome this barrier. Towards this, in our pilot task organized at TREC 2024, we introduced the task of reference attribution as a means to mitigate the generation of false statements by LLMs answering biomedical questions. |
Deepak Gupta; Dina Demner-Fushman; William Hersh; Steven Bedrick; Kirk Roberts; | arxiv-cs.IR | 2024-11-27 |
| 382 | Task Progressive Curriculum Learning for Robust Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we show for the first time that robust Visual Question Answering is attainable by simply enhancing the training strategy. |
AHMED AKL et. al. | arxiv-cs.CV | 2024-11-26 |
| 383 | Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The goal of our survey is to provide an overview of the development of VQA and a detailed description of the latest models with high timeliness. |
JIAYI KUANG et. al. | arxiv-cs.CL | 2024-11-26 |
| 384 | Text-Guided Coarse-to-Fine Fusion Network for Robust Remote Sensing Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a Text-guided Coarse-to-Fine Fusion Network (TGFNet), which leverages the semantic relationships between question text and multi-source images to guide the network toward complementary fusion at the feature level. |
ZHICHENG ZHAO et. al. | arxiv-cs.CV | 2024-11-24 |
| 385 | AfriMed-QA: A Pan-African, Multi-Specialty, Medical Question-Answering Benchmark Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce AfriMed-QA, the first large scale Pan-African English multi-specialty medical Question-Answering (QA) dataset, 15,000 questions (open and closed-ended) sourced from over 60 medical schools across 16 countries, covering 32 medical specialties. |
TOBI OLATUNJI et. al. | arxiv-cs.CL | 2024-11-23 |
| 386 | VideoEspresso: A Large-Scale Chain-of-Thought Dataset for Fine-Grained Video Reasoning Via Core Frame Selection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To exploit the potential of high-quality VideoQA pairs, we propose a Hybrid LVLMs Collaboration framework, featuring a Frame Selector and a two-stage instruction fine-tuned reasoning LVLM. |
SONGHAO HAN et. al. | arxiv-cs.CV | 2024-11-22 |
| 387 | KTMN: Knowledge-driven Two-stage Modulation Network for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
Jingya Shi; Dezhi Han; Chongqing Chen; Xiang Shen; | Multim. Syst. | 2024-11-20 |
| 388 | Retrieval-Augmented Generation for Domain-Specific Question Answering: A Case Study on Pittsburgh and CMU Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We designed a Retrieval-Augmented Generation (RAG) system to provide large language models with relevant documents for answering domain-specific questions about Pittsburgh and Carnegie Mellon University (CMU). |
Haojia Sun; Yaqi Wang; Shuting Zhang; | arxiv-cs.LG | 2024-11-20 |
| 389 | Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We demonstrate how simple, training-free, token-level disambiguation methods may be effectively used to improve LLM performance for ambiguous question answering tasks. |
Aryan Keluskar; Amrita Bhattacharjee; Huan Liu; | arxiv-cs.CL | 2024-11-19 |
| 390 | Neon: News Entity-Interaction Extraction for Enhanced Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the information modeled by the parametric memory of LLMs is often outdated, and Web results from prototypical retrieval systems may fail to capture the latest relevant information and struggle to handle conflicting reports in evolving news. To address this challenge, we present the NEON framework, designed to extract emerging entity interactions — such as events or activities — as described in news articles. |
Sneha Singhania; Silviu Cucerzan; Allen Herring; Sujay Kumar Jauhar; | arxiv-cs.CL | 2024-11-19 |
| 391 | Mitigating Knowledge Conflicts in Language Model-Driven Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We specifically target a common hallucination pattern in question answering, examining how the correspondence between entities and their contexts during model training influences the system’s performance at inference time. |
HAN CAO et. al. | arxiv-cs.CL | 2024-11-18 |
| 392 | A Comprehensive Survey on Visual Question Answering Datasets and Algorithms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Since the inception of this field, a plethora of VQA datasets and models have been published. In this article, we meticulously analyze the current state of VQA datasets and models, while cleanly dividing them into distinct categories and then summarizing the methodologies and characteristics of each category. |
Raihan Kabir; Naznin Haque; Md Saiful Islam; | arxiv-cs.CV | 2024-11-17 |
| 393 | Memory-Augmented Multimodal LLMs for Surgical VQA Via Self-Contained Inquiry Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these methods often struggle with limited scene understanding and question comprehension, and some rely on external resources (e.g., pre-extracted object features), which can introduce errors and generalize poorly across diverse surgical environments. To address these challenges, we propose SCAN, a simple yet effective memory-augmented framework that leverages Multimodal LLMs to improve surgical context comprehension via Self-Contained Inquiry. |
WENJUN HOU et. al. | arxiv-cs.CV | 2024-11-16 |
| 394 | Understanding Multimodal LLMs: The Mechanistic Interpretability of Llava in Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we apply mechanistic interpretability methods to analyze the visual question answering (VQA) mechanisms in the first MLLM, Llava. |
Zeping Yu; Sophia Ananiadou; | arxiv-cs.CL | 2024-11-16 |
| 395 | A Benchmark for Long-Form Medical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce a new publicly available benchmark featuring real-world consumer medical questions with long-form answer evaluations annotated by medical doctors. |
PEDRAM HOSSEINI et. al. | arxiv-cs.CL | 2024-11-14 |
| 396 | Comprehensive and Practical Evaluation of Retrieval-Augmented Generation Systems for Medical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper addresses this gap by providing a comprehensive evaluation framework for medical question-answering (QA) systems in a RAG setting for these situations, including sufficiency, integration, and robustness. We introduce Medical Retrieval-Augmented Generation Benchmark (MedRGB) that provides various supplementary elements to four medical QA datasets for testing LLMs’ ability to handle these specific scenarios. |
Nghia Trung Ngo; Chien Van Nguyen; Franck Dernoncourt; Thien Huu Nguyen; | arxiv-cs.CL | 2024-11-14 |
| 397 | The Limited Impact of Medical Adaptation of Large Language and Vision-Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we compare ten medical LLMs and two VLMs against their corresponding base models, arriving at a different conclusion: all medical VLMs and nearly all medical LLMs fail to consistently improve over their base models in the zero-/few-shot prompting and supervised fine-tuning regimes for medical question answering (QA). |
Daniel P. Jeong; Pranav Mani; Saurabh Garg; Zachary C. Lipton; Michael Oberst; | arxiv-cs.CL | 2024-11-13 |
| 398 | Deceiving Question-Answering Models: A Hybrid Word-Level Adversarial Approach Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces QA-Attack (Question Answering Attack), a novel word-level adversarial strategy that fools QA models. |
Jiyao Li; Mingze Ni; Yongshun Gong; Wei Liu; | arxiv-cs.CL | 2024-11-12 |
| 399 | Unlocking Markets: A Multilingual Benchmark to Cross-Market Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce a large-scale dataset comprising over 7 million questions from 17 marketplaces across 11 languages. |
Yifei Yuan; Yang Deng; Anders S�gaard; Mohammad Aliannejadi; | emnlp | 2024-11-11 |
| 400 | Large Language Models Are Poor Clinical Decision-Makers: A Comprehensive Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To better understand LLMs in the clinic, we construct a benchmark ClinicBench. |
FENGLIN LIU et. al. | emnlp | 2024-11-11 |
| 401 | DVD: Dynamic Contrastive Decoding for Knowledge Amplification in Multi-Document Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Retrieval-augmented generation (RAG) offers a potential remedy, yet the uneven retrieval quality and irrelevant contents may distract LLMs. In this work, we address these issues at the generation phase by treating RAG as a multi-document QA task. |
Jing Jin; Houfeng Wang; Hao Zhang; Xiaoguang Li; Zhijiang Guo; | emnlp | 2024-11-11 |
| 402 | Training-free Deep Concept Injection Enables Language Models for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we make the first attempt to demonstrate that the PLM is able to perform zero-shot crossmodal tasks without any crossmodal pretraining, when the observed visual concepts are injected as both additional input text tokens and augmentation in the intermediate features within each feed-forward network for the PLM. |
Xudong Lin; Manling Li; Richard Zemel; Heng Ji; Shih-Fu Chang; | emnlp | 2024-11-11 |
| 403 | Toward Optimal Search and Retrieval for RAG Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Here, we work towards the goal of understanding how retrievers can be optimized for RAG pipelines for common tasks such as Question Answering (QA). |
ALEXANDRIA LETO et. al. | arxiv-cs.CL | 2024-11-11 |
| 404 | MILD Bot: Multidisciplinary Childhood Cancer Survivor Question-Answering Bot Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study introduces a Multidisciplinary chILDhood cancer survivor question-answering (MILD) bot designed to support childhood cancer survivors facing diverse challenges in their survivorship journey. |
MIRAE KIM et. al. | emnlp | 2024-11-11 |
| 405 | RAG4ITOps: A Supervised Fine-Tunable and Comprehensive RAG Framework for IT Operations and Maintenance Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a general and comprehensive framework based on Retrieval Augmented Generation (RAG) and facilitate the whole business process of establishing QA systems for IT operations and maintenance. |
TIANYANG ZHANG et. al. | emnlp | 2024-11-11 |
| 406 | Self-Bootstrapped Visual-Language Model for Knowledge Selection and Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Thus, the retrieved knowledge is not truly conducive to helping answer the question, affecting the performance of the overall system. To address this issue, we propose a novel framework that leverages the visual-language model to select the key knowledge retrieved by DPR and answer questions. |
Dongze Hao; Qunbo Wang; Longteng Guo; Jie Jiang; Jing Liu; | emnlp | 2024-11-11 |
| 407 | ERVQA: A Dataset to Benchmark The Readiness of Large Vision Language Models in Hospital Environments Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce the Emergency Room Visual Question Answering (ERVQA) dataset, consisting of |
SOURJYADIP RAY et. al. | emnlp | 2024-11-11 |
| 408 | SciDQA: A Deep Reading Comprehension Dataset Over Scientific Papers Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce SciDQA, a new dataset for reading comprehension that challenges language models to deeply understand scientific articles, consisting of 2,937 QA pairs. |
Shruti Singh; Nandan Sarkar; Arman Cohan; | emnlp | 2024-11-11 |
| 409 | OMG-QA: Building Open-Domain Multi-Modal Generative Question Answering Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce OMG-QA, a new resource for question answering that is designed to evaluate the effectiveness of question answering systems that perform retrieval augmented generation (RAG) in scenarios that demand reasoning on multi-modal, multi-document contexts. |
LINYONG NAN et. al. | emnlp | 2024-11-11 |
| 410 | Efficient Answer Retrieval System (EARS): Combining Local DB Search and Web Search for Generative QA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose an efficient answer retrieval system **EARS**: a production-ready, factual question answering (QA) system that combines local knowledge base search with generative, context-based QA. |
Nikita Krayko; Ivan Sidorov; Fedor Laputin; Daria Galimzianova; Vasily Konovalov; | emnlp | 2024-11-11 |
| 411 | StorySparkQA: Expert-Annotated QA Pairs with Real-World Knowledge for Children’s Story-Based Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This limitation can be attributed to the existing question-answering (QA) datasets used for children’s education, upon which the systems are built, failing to capture the nuances of how education experts think when conducting interactive story reading activities. To bridge this gap, we design an annotation framework, empowered by existing knowledge graph to capture experts’ annotations and thinking process, and leverage this framework to construct StorySparkQA dataset, which comprises 5, 868 expert-annotated QA pairs with real-world knowledge. |
JIAJU CHEN et. al. | emnlp | 2024-11-11 |
| 412 | Model Internals-based Answer Attribution for Trustworthy Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we present MIRAGE – Model Internals-based RAG Explanations – a plug-and-play approach using model internals for faithful answer attribution in RAG applications. |
Jirui Qi; Gabriele Sarti; Raquel Fern�ndez; Arianna Bisazza; | emnlp | 2024-11-11 |
| 413 | Empowering Large Language Model for Continual Video Question Answering with Collaborative Prompting Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we explore the novel challenge of VideoQA within a continual learning framework, and empirically identify a critical issue: fine-tuning a large language model (LLM) for a sequence of tasks often results in catastrophic forgetting. |
CHEN CAI et. al. | emnlp | 2024-11-11 |
| 414 | Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing benchmarks employ irrelevant noise texts to artificially extend the length of test cases, diverging from the real-world scenarios of long-context applications. To bridge this gap, we propose a novel long-context benchmark, Loong, aligning with realistic scenarios through extended multi-document question answering (QA). |
MINZHENG WANG et. al. | emnlp | 2024-11-11 |
| 415 | Adaptive Question Answering: Enhancing Language Model Proficiency for Addressing Knowledge Conflicts with Source Citations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the importance of both aspects, no prior research has combined them, leaving a significant gap in the development of QA systems. In this work, we bridge this gap by proposing the novel task of QA with source citation in ambiguous settings, where multiple valid answers exist. |
Sagi Shaier; Ari Kobren; Philip V. Ogren; | emnlp | 2024-11-11 |
| 416 | Multi-Level Information Retrieval Augmented Generation for Knowledge-based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a multi-level information RAG approach that enhances answer generation through entity retrieval and query expansion. |
Adjali Omar; Olivier Ferret; Sahar Ghannay; Herv� Le Borgne; | emnlp | 2024-11-11 |
| 417 | CompAct: Compressing Retrieved Documents Actively for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Context compression tackles this issue by filtering out irrelevant information, but current methods still struggle in realistic scenarios where crucial information cannot be captured with a single-step approach. To overcome this limitation, we introduce CompAct, a novel framework that employs an active strategy to condense extensive documents without losing key information. |
Chanwoong Yoon; Taewhoo Lee; Hyeon Hwang; Minbyul Jeong; Jaewoo Kang; | emnlp | 2024-11-11 |
| 418 | You Make Me Feel Like A Natural Question: Training QA Systems on Transformed Trivia Questions Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Training question-answering QA and information retrieval systems for web queries require large, expensive datasets that are difficult to annotate and time-consuming to gather. … |
TASNIM KABIR et. al. | emnlp | 2024-11-11 |
| 419 | EfficientRAG: Efficient Retriever for Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce EfficientRAG, an efficient retriever for multi-hop question answering. |
ZIYUAN ZHUANG et. al. | emnlp | 2024-11-11 |
| 420 | A Simple LLM Framework for Long-Range Video Question-Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present LLoVi, a simple yet effective **L**anguage-based **Lo**ng-range **Vi**deo question-answering (LVQA) framework. |
CE ZHANG et. al. | emnlp | 2024-11-11 |
| 421 | Encoding and Controlling Global Semantics for Long-form Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To further enhance the controllability, we introduce a cross-modal compositional congruence objective to encourage global semantics aligned with the question. |
THONG THANH NGUYEN et. al. | emnlp | 2024-11-11 |
| 422 | REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Despite the extensive efforts on RAG research, in existing methods, LLMs cannot precisely assess the relevance of retrieved documents, thus likely leading to misleading or even incorrect utilization of external knowledge (i. e. , retrieved documents). To address this issue, in this paper, we propose REAR, a RElevance-Aware Retrieval-augmented approach for open-domain question answering (QA). |
YUHAO WANG et. al. | emnlp | 2024-11-11 |
| 423 | RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, most existing datasets for this task are either constructed using a single source corpus or consist of short extractive answers, which fall short of evaluating large language model (LLM) based RAG-QA systems on cross-domain generalization. To address these limitations, we create Long-form RobustQA (LFRQA), a new dataset comprising human-written long-form answers that integrate short extractive answers from multiple documents into a single, coherent narrative, covering 26K queries and large corpora across seven different domains. |
RUJUN HAN et. al. | emnlp | 2024-11-11 |
| 424 | Right for Right Reasons: Large Language Models for Verifiable Commonsense Knowledge Graph Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In response, we propose Right for Right Reasons (R3), a commonsense KGQA methodology that allows for a verifiable reasoning procedure by axiomatically surfacing intrinsic commonsense knowledge of LLMs and grounding every factual reasoning step on KG triples. |
Armin Toroghi; Willis Guo; Mohammad Mahdi Abdollah Pour; Scott Sanner; | emnlp | 2024-11-11 |
| 425 | PCQPR: Proactive Conversational Question Planning with Reflection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we redefine the CQG task as Conclusion-driven Conversational Question Generation (CCQG) by focusing on proactivity, not merely reacting to the unfolding conversation but actively steering it towards a conclusion-oriented question-answer pair. To address this, we propose a novel approach, called Proactive Conversational Question Planning with self-Refining (PCQPR). |
Shasha Guo; Lizi Liao; Jing Zhang; Cuiping Li; Hong Chen; | emnlp | 2024-11-11 |
| 426 | Generate-on-Graph: Treat LLM As Both Agent and KG for Incomplete Knowledge Graph Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To handle IKGQA, we propose a training-free method called Generate-on-Graph (GoG), which can generate new factual triples while exploring KGs. |
YAO XU et. al. | emnlp | 2024-11-11 |
| 427 | Revisiting Automated Evaluation for Long-form Table Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce LFTQA-Eval, a meta-evaluation dataset comprising 2,988 human-annotated examples, to rigorously assess the efficacy of current automated metrics in assessing LLM-based LFTQA systems, with a focus on faithfulness and comprehensiveness. |
Yuqi Wang; Lyuhao Chen; Songcheng Cai; Zhijian Xu; Yilun Zhao; | emnlp | 2024-11-11 |
| 428 | Evidence-Focused Fact Summarization for Knowledge-Augmented Zero-Shot Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing methods, like concatenation or free-form textual conversion of triples, have limitations, including duplicated entities or relations, reduced evidence density, and failure to highlight crucial evidence. To address these issues, we propose EFSum, an Evidence-focused Fact Summarization framework for enhanced QA with knowledge-augmented LLMs. |
Sungho Ko; Hyunjin Cho; Hyungjoo Chae; Jinyoung Yeo; Dongha Lee; | emnlp | 2024-11-11 |
| 429 | Where Am I? Large Language Models Wandering Between Semantics and Structures in Long Contexts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To verify LLMs’ task alignment, we introduce a verification framework and resources considering both semantic relevancy and structural diversity of the given long context knowledge. |
Seonmin Koo; Jinsung Kim; YoungJoon Jang; Chanjun Park; Heuiseok Lim; | emnlp | 2024-11-11 |
| 430 | Do Great Minds Think Alike? Investigating Human-AI Complementarity in Question Answering with CAIMIRA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recent advancements of large language models (LLMs)have led to claims of AI surpassing humansin natural language processing NLP tasks such as textual understanding and reasoning. %This work investigates these assertions by introducingCAIMIRA, a novel framework rooted in item response theory IRTthat enables quantitative assessment and comparison of problem-solving abilities inquestion-answering QA agents. |
Maharshi Gor; Hal Daum� Iii; Tianyi Zhou; Jordan Lee Boyd-Graber; | emnlp | 2024-11-11 |
| 431 | Contextualized Sequence Likelihood: Enhanced Confidence Scores for Natural Language Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose enhancing the predicted sequence probability by assigning different weights to various tokens using attention values elicited from the base LLM. |
Zhen Lin; Shubhendu Trivedi; Jimeng Sun; | emnlp | 2024-11-11 |
| 432 | Towards Faithful Knowledge Graph Explanation Through Deep Alignment in Commonsense Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We identify confounding effects and LM-KG misalignment as key factors causing spurious explanations. To address this, we introduce the LM-KG Fidelity metric to assess KG representation reliability and propose the LM-KG Distribution-aware Alignment (LKDA) algorithm to improve explanation faithfulness. |
Weihe Zhai; Arkaitz Zubiaga; Bingquan Liu; Chengjie Sun; Yalong Zhao; | emnlp | 2024-11-11 |
| 433 | EVQAScore: A Fine-grained Metric for Video Question Answering Data Quality Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although various methods have been proposed for assessing video caption quality, there remains a lack of dedicated evaluation methods for Video QA. To address this gap, we introduce EVQAScore, a reference-free method that leverages keyword extraction to assess both video caption and video QA data quality. |
Hao Liang; Zirong Chen; Hejun Dong; Wentao Zhang; | arxiv-cs.CV | 2024-11-11 |
| 434 | Pre-training Cross-lingual Open Domain Question Answering with Large-scale Synthetic Supervision Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View |
Fan Jiang; Tom Drummond; Trevor Cohn; | emnlp | 2024-11-11 |
| 435 | RAC: Retrieval-augmented Conversation Dataset for Open-domain Question Answering in Conversational Settings Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present a novel retrieval-augmented conversation (RAC) dataset and develop a baseline system comprising query rewriting, retrieval, reranking, and response generation stages. |
Bonggeun Choi; JeongJae Park; Yoonsung Kim; Jaehyun Park; Youngjoong Ko; | emnlp | 2024-11-11 |
| 436 | Can LLM Generate Culturally Relevant Commonsense QA Data? Case Study in Indonesian and Sundanese IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we investigate the effectiveness of using LLMs in generating culturally relevant commonsense QA datasets for Indonesian and Sundanese languages. |
Rifki Afina Putri; Faiz Ghifari Haznitrama; Dea Adhista; Alice Oh; | emnlp | 2024-11-11 |
| 437 | LONGAGENT: Achieving Question Answering for 128k-Token-Long Documents Through Multi-Agent Collaboration Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce _LongAgent_, a multi-agent collaboration method that enables efficient and effective QA over 128k-token-long documents. |
JUN ZHAO et. al. | emnlp | 2024-11-11 |
| 438 | Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we compare seven public medical LLMs and two VLMs against their corresponding base models, arriving at a different conclusion: all medical VLMs and nearly all medical LLMs fail to consistently improve over their base models in the zero-/few-shot prompting regime for medical question-answering (QA) tasks. |
Daniel P Jeong; Saurabh Garg; Zachary Chase Lipton; Michael Oberst; | emnlp | 2024-11-11 |
| 439 | Cross-lingual Transfer for Automatic Question Generation By Learning Interrogative Structures in Target Languages Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a simple and efficient XLT-QG method that operates without the need for monolingual, parallel, or labeled data in the target language, utilizing a small language model. |
Seonjeong Hwang; Yunsu Kim; Gary Lee; | emnlp | 2024-11-11 |
| 440 | Triad: A Framework Leveraging A Multi-Role LLM-based Agent to Solve Knowledge Base Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present Triad, a unified framework that utilizes an LLM-based agent with multiple roles for KBQA tasks. |
CHANG ZONG et. al. | emnlp | 2024-11-11 |
| 441 | RE-RAG: Improving Open-Domain QA Performance and Interpretability with Relevance Estimator in Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a weakly supervised method for training the RE simply utilizing question-answer data without any labels for correct contexts. |
Kiseung Kim; Jay-Yoon Lee; | emnlp | 2024-11-11 |
| 442 | GOVERN: Gradient Orientation Vote Ensemble for Multi-Teacher Reinforced Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, for practical deployment, it is crucial to perform knowledge distillation to maintain high performance while operating under computational constraints. In this paper, we address a key question: given the importance of unsupervised distillation for student model performance, how can knowledge from multiple teacher models be effectively ensemble during this stage without the guidance of labels? |
WENJIE ZHOU et. al. | emnlp | 2024-11-11 |
| 443 | ZEBRA: Zero-Shot Example-Based Retrieval Augmentation for Commonsense Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, these methods require additional training, hand-crafted templates or human-written explanations. To address these issues, we introduce ZEBRA, a zero-shot question answering framework that combines retrieval, case-based reasoning and introspection and dispenses with the need for additional training of the LLM. |
Francesco Maria Molfese; Simone Conia; Riccardo Orlando; Roberto Navigli; | emnlp | 2024-11-11 |
| 444 | PDFTriage: Question Answering Over Long, Structured Documents IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: When a system has to query the document for context, this incongruity is brought to the fore, and seemingly trivial questions can trip up the QA system. To bridge this fundamental gap in handling structured documents, we propose an approach called PDFTriage that enables models to retrieve the context based on either structure or content. |
JON SAAD-FALCON et. al. | emnlp | 2024-11-11 |
| 445 | SparrowVQE: Visual Question Explanation for Course Content Understanding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper aims to advance the field by introducing Visual Question Explanation (VQE), which enhances the ability of VQA to provide detailed explanations rather than brief responses and address the need for more complex interaction with visual content. |
Jialu Li; Manish Kumar Thota; Ruslan Gokhman; Radek Holik; Youshan Zhang; | arxiv-cs.CV | 2024-11-11 |
| 446 | TraveLER: A Modular Multi-LMM Agent Framework for Video Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Currently, existing methods perform all of these steps in a single pass without being able to adapt if insufficient or incorrect information is collected. To overcome this, we introduce a modular multi-LMM agent framework based on several agents with different roles, instructed by a Planner agent that updates its instructions using shared feedback from the other agents. |
Chuyi Shang; Amos You; Sanjay Subramanian; Trevor Darrell; Roei Herzig; | emnlp | 2024-11-11 |
| 447 | CommVQA: Situating Visual Question Answering in Communicative Contexts Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To evaluate how situating images within naturalistic contexts shapes visual questions, we introduce CommVQA, a VQA dataset consisting of images, image descriptions, real-world communicative scenarios where the image might appear (e. g. , a travel website), and follow-up questions and answers conditioned on the scenario and description. |
Nandita Shankar Naik; Christopher Potts; Elisa Kreiss; | emnlp | 2024-11-11 |
| 448 | GUIDEQ: Framework for Guided Questioning for Progressive Informational Collection and Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our work, GUIDEQ, presents a novel framework for asking guided questions to further progress a partial information. |
Priya Mishra; Suraj Racha; Kaustubh Ponkshe; Adit Akarsh; Ganesh Ramakrishnan; | arxiv-cs.CL | 2024-11-08 |
| 449 | SaSR-Net: Source-Aware Semantic Representation Network for Enhancing Audio-Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce the Source-aware Semantic Representation Network (SaSR-Net), a novel model designed for AVQA. |
TIANYU YANG et. al. | arxiv-cs.CV | 2024-11-07 |
| 450 | MEG: Medical Knowledge-Augmented Large Language Models for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we present MEG, a parameter-efficient approach for medical knowledge-augmented LLMs. |
Laura Cabello; Carmen Martin-Turrero; Uchenna Akujuobi; Anders Søgaard; Carlos Bobed; | arxiv-cs.CL | 2024-11-06 |
| 451 | Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we compare seven public medical LLMs and two VLMs against their corresponding base models, arriving at a different conclusion: all medical VLMs and nearly all medical LLMs fail to consistently improve over their base models in the zero-/few-shot prompting regime for medical question-answering (QA) tasks. |
Daniel P. Jeong; Saurabh Garg; Zachary C. Lipton; Michael Oberst; | arxiv-cs.CL | 2024-11-06 |
| 452 | Lexicalization Is All You Need: Examining The Impact of Lexical Knowledge in A Compositional QALD System Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we examine the impact of lexicalization on Question Answering over Linked Data (QALD). |
David Maria Schmidt; Mohammad Fazleh Elahi; Philipp Cimiano; | arxiv-cs.AI | 2024-11-06 |
| 453 | VQA$^2$: Visual Question Answering for Video Quality Assessment Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Nevertheless, related work has not been explored in the video domain, leaving substantial room for improvement. To address this gap, we introduce the VQA2 Instruction Dataset – the first visual question answering instruction dataset that focuses on video quality assessment. |
ZIHENG JIA et. al. | arxiv-cs.CV | 2024-11-06 |
| 454 | Leveraging Large Language Models in Code Question Answering: Baselines and Issues Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents a work devoted to using large language models for question answering over source code in Python. |
Georgy Andryushchenko; Vladimir Ivanov; Vladimir Makharev; Elizaveta Tukhtina; Aidar Valeev; | arxiv-cs.CL | 2024-11-05 |
| 455 | FactTest: Factuality Testing in Large Language Models with Finite-Sample and Distribution-Free Guarantees Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce FactTest, a novel framework that statistically assesses whether a LLM can confidently provide correct answers to given questions with high-probability correctness guarantees. |
FAN NIE et. al. | arxiv-cs.CL | 2024-11-04 |
| 456 | Multimodal Commonsense Knowledge Distillation for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel graph-based multimodal commonsense knowledge distillation framework that constructs a unified relational graph over commonsense knowledge, visual objects and questions through a Graph Convolutional Network (GCN) following a teacher-student environment. |
Shuo Yang; Siwen Luo; Soyeon Caren Han; | arxiv-cs.CL | 2024-11-04 |
| 457 | One VLM to Keep It Learning: Generation and Balancing for Data-free Continual Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose the first data-free method that leverages the language generation capability of a VLM, instead of relying on external models, to produce pseudo-rehearsal data for addressing continual VQA. |
Deepayan Das; Davide Talon; Massimiliano Mancini; Yiming Wang; Elisa Ricci; | arxiv-cs.CV | 2024-11-04 |
| 458 | A Visual Question Answering Method for SAR Ship: Breaking The Requirement for Multimodal Dataset Construction and Model Fine-Tuning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This has greatly hindered the application of VQA to downstream tasks, such as ship information analysis based on Synthetic Aperture Radar (SAR) imagery. To address this challenge, this letter proposes a novel VQA approach that integrates object detection networks with visual language models, specifically designed for analyzing ships in SAR images. |
Fei Wang; Chengcheng Chen; Hongyu Chen; Yugang Chang; Weiming Zeng; | arxiv-cs.CV | 2024-11-03 |
| 459 | Diagnosing Medical Datasets with Training Dynamics Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This study explores the potential of using training dynamics as an automated alternative to human annotation for evaluating the quality of training data. |
Laura Wenderoth; | arxiv-cs.LG | 2024-11-03 |
| 460 | Goal-Oriented Semantic Communication for Wireless Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Meanwhile, this brings new communication challenges between the local and edge, including limited bandwidth, channel noise, and multipath effects, which degrade VQA performance and user quality of experience (QoE), particularly during the transmission of large high-resolution images. To overcome these bottlenecks, we propose a goal-oriented semantic communication (GSC) framework that focuses on effectively extracting and transmitting semantic information most relevant to the VQA goals, improving the answering accuracy and enhancing the effectiveness and efficiency. |
Sige Liu; Nan Li; Yansha Deng; Tony Q. S. Quek; | arxiv-cs.CV | 2024-11-03 |
| 461 | GRS-QA — Graph Reasoning-Structured Question Answering Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the impact of the inherent reasoning structures on LLM M-QA performance remains unclear, largely due to the absence of QA datasets that provide fine-grained reasoning structures. To address this gap, we introduce the Graph Reasoning-Structured Question Answering Dataset (GRS-QA), which includes both semantic contexts and reasoning structures for QA pairs. |
ANISH PAHILAJANI et. al. | arxiv-cs.CL | 2024-11-01 |
| 462 | Multi-Modal Validation and Domain Interaction Learning for Knowledge-Based Visual Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Knowledge-based Visual Question Answering (KB-VQA) aims to answer the image-aware question via the external knowledge, which requires an agent to not only understand images but … |
Ning Xu; Yifei Gao; An-An Liu; Hongshuo Tian; Yongdong Zhang; | IEEE Transactions on Knowledge and Data Engineering | 2024-11-01 |
| 463 | Right This Way: Can VLMs Guide Us to See More to Answer Questions? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This capability is especially valuable for assisting visually impaired individuals who often need guidance to capture images correctly. To evaluate this capability of current VLMs, we introduce a human-labeled dataset as a benchmark for this task. |
LI LIU et. al. | arxiv-cs.CV | 2024-11-01 |
| 464 | Enhancing Question Answering Precision with Optimized Vector Retrieval and Instructions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose an innovative approach to improve QA task performances by integrating optimized vector retrievals and instruction methodologies. |
Lixiao Yang; Mengyang Xu; Weimao Ke; | arxiv-cs.IR | 2024-11-01 |
| 465 | LRCN: Layer-residual Co-Attention Networks for Visual Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
DEZHI HAN et. al. | Expert Syst. Appl. | 2024-11-01 |
| 466 | The Question Answering System GeoQA2 and A New Benchmark for Its Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View |
SERGIOS-ANESTIS KEFALIDIS et. al. | Int. J. Appl. Earth Obs. Geoinformation | 2024-11-01 |
| 467 | A Confidence-based Knowledge Integration Framework for Cross-domain Table Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
YUANKAI FAN et. al. | Knowl. Based Syst. | 2024-11-01 |
| 468 | Show Me What and Where Has Changed? Question Answering and Grounding for Remote Sensing Change Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce a new task named Change Detection Question Answering and Grounding (CDQAG), which extends the traditional change detection task by providing interpretable textual answers and intuitive visual evidence. |
KE LI et. al. | arxiv-cs.CV | 2024-10-31 |
| 469 | Dynamic Strategy Planning for Efficient Question Answering with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In our work, we propose a novel technique DyPlan, to induce a dynamic strategy selection process in LLMs, to improve performance and reduce costs in question-answering. |
Tanmay Parekh; Pradyot Prakash; Alexander Radovic; Akshay Shekher; Denis Savenkov; | arxiv-cs.CL | 2024-10-30 |
| 470 | SimpsonsVQA: Enhancing Inquiry-Based Learning with A Tailored Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Hence, in this paper, we present SimpsonsVQA, a novel dataset for VQA derived from The Simpsons TV show, designed to promote inquiry-based learning. |
Ngoc Dung Huynh; Mohamed Reda Bouadjenek; Sunil Aryal; Imran Razzak; Hakim Hacid; | arxiv-cs.CV | 2024-10-29 |
| 471 | Are VLMs Really Blind Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, these models fail to perform well on low-level basic visual tasks which are especially easy for humans. Our goal in this work was to determine if these models are truly blind to geometric reasoning or if there are ways to enhance their capabilities in this area. |
Ayush Singh; Mansi Gupta; Shivank Garg; | arxiv-cs.CL | 2024-10-29 |
| 472 | Enhancing Financial Question Answering with A Multi-Agent Reflection Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we propose a multi-agent framework incorporating a critic agent that reflects on the reasoning steps and final answers for each question. |
Sorouralsadat Fatemi; Yuheng Hu; | arxiv-cs.CL | 2024-10-29 |
| 473 | RealCQA-V2 : Visual Premise Proving A Manual COT Dataset for Charts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce Visual Premise Proving (VPP), a novel task tailored to refine the process of chart question answering by deconstructing it into a series of logical premises. |
Saleem Ahmed; Ranga Setlur; Venu Govindaraju; | arxiv-cs.AI | 2024-10-29 |
| 474 | CT2C-QA: Multimodal Question Answering Over Chinese Text, Table and Chart Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present C$\text{T}^2$C-QA, a pioneering Chinese reasoning-based QA dataset that includes an extensive collection of text, tables, and charts, meticulously compiled from 200 selectively sourced webpages. |
BOWEN ZHAO et. al. | arxiv-cs.CL | 2024-10-28 |
| 475 | Few-Shot Multimodal Explanation for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View |
Dizhan Xue; Shengsheng Qian; Changsheng Xu; | ACM Multimedia | 2024-10-28 |
| 476 | Combating Visual Question Answering Hallucinations Via Robust Multi-Space Co-Debias Learning Related Papers Related Patents Related Grants Related Venues Related Experts View |
JIAWEI ZHU et. al. | ACM Multimedia | 2024-10-28 |
| 477 | 3D Question Answering with Scene Graph Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View |
ZIZHAO WU et. al. | ACM Multimedia | 2024-10-28 |
| 478 | CREAM: Coarse-to-Fine Retrieval and Multi-modal Efficient Tuning for Document VQA Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Document Visual Question Answering (DVQA) involves responding to queries based on the contents of document images. Existing works are confined to locating information within a … |
Jinxu Zhang; Yong Yu; Yu Zhang; | ACM Multimedia | 2024-10-28 |
| 479 | SandboxAQ’s Submission to MRL 2024 Shared Task on Multi-lingual Multi-task Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper explores the problems of Question Answering (QA) and Named Entity Recognition (NER) in five diverse languages. |
Isidora Chara Tourni; Sayontan Ghosh; Brenda Miao; Constantijn van der Poel; | arxiv-cs.CL | 2024-10-28 |
| 480 | Causal-driven Large Language Models with Faithful Reasoning for Knowledge Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In Large Language Models (LLMs), text generation that involves knowledge representation is often fraught with the risk of “halluci-nations”, where models confidently produce … |
JIAWEI WANG et. al. | ACM Multimedia | 2024-10-28 |
| 481 | Get Large Language Models Ready to Speak: A Late-fusion Approach for Speech Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce a text-to-speech (TTS) system powered by a fine-tuned Llama model, named TTS-Llama, that achieves state-of-the-art speech synthesis performance. |
MAOHAO SHEN et. al. | arxiv-cs.CL | 2024-10-27 |
| 482 | EfficientEQA: An Efficient Approach for Open Vocabulary Embodied Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In real-world scenarios, a robotic agent must efficiently explore and accurately answer questions in open-vocabulary settings. To address these challenges, we propose a novel framework called EfficientEQA for open-vocabulary EQA, which enables efficient exploration and accurate answering. |
KAI CHENG et. al. | arxiv-cs.RO | 2024-10-26 |
| 483 | Sensor2Text: Enabling Natural Language Interactions for Daily Activity Tracking Using Wearable Sensors Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents Sensor2Text, a model proficient in tracking daily activities and engaging in conversations using wearable sensors. |
Wenqiang Chen; Jiaxuan Cheng; Leyao Wang; Wei Zhao; Wojciech Matusik; | arxiv-cs.LG | 2024-10-25 |
| 484 | Decoding on Graphs: Faithful and Sound Reasoning on Knowledge Graphs Through Generation of Well-Formed Chains Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present DoG (Decoding on Graphs), a novel framework that facilitates a deep synergy between LLMs and KGs. |
KUN LI et. al. | arxiv-cs.CL | 2024-10-24 |
| 485 | An Adaptive Framework for Generating Systematic Explanatory Answer in Online Q&A Platforms Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The pioneering task is defined as explanatory answer generation, which entails handling identified challenges such as the requirement for comprehensive information and logical coherence within the generated context. To address these issues, we refer to systematic thinking theory and propose SynthRAG, an innovative framework designed to enhance QA performance. |
ZIYANG CHEN et. al. | arxiv-cs.CL | 2024-10-23 |
| 486 | Leveraging The Domain Adaptation of Retrieval Augmented Generation Models for Question Answering and Reducing Hallucination Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigated the performance of diverse RAG and RAG-like architectures through domain adaptation and evaluated their ability to generate accurate and relevant response grounded in the contextual knowledge base. |
Salman Rakin; Md. A. R. Shibly; Zahin M. Hossain; Zeeshan Khan; Md. Mostofa Akbar; | arxiv-cs.CL | 2024-10-23 |
| 487 | Aggregated Knowledge Model: Enhancing Domain-Specific QA with Fine-Tuned and Retrieval-Augmented Generation Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a novel approach to enhancing closed-domain Question Answering (QA) systems, focusing on the specific needs of the Lawrence Berkeley National Laboratory (LBL) Science Information Technology (ScienceIT) domain. |
Fengchen Liu; Jordan Jung; Wei Feinstein; Jeff DAmbrogia; Gary Jung; | arxiv-cs.CL | 2024-10-23 |
| 488 | Graphusion: A RAG Framework for Knowledge Graph Construction with A Global Perspective Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This work introduces Graphusion, a zero-shot KGC framework from free text. |
RUI YANG et. al. | arxiv-cs.CL | 2024-10-23 |
| 489 | SG-FSM: A Self-Guiding Zero-Shot Prompting Paradigm for Multi-Hop Question Answering Based on Finite State Machine Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, Multi-hop Question Answering (MHQA) remains challenging for many existing models due to issues like hallucination, error propagation, and limited context length. To address these challenges and enhance LLMs’ performance on MHQA, we propose the Self-Guiding prompting Finite State Machine (SG-FSM), designed to strengthen multi-hop reasoning abilities. |
XIAOCHEN WANG et. al. | arxiv-cs.CL | 2024-10-22 |
| 490 | Correct After Answer: Enhancing Multi-Span Question Answering with Post-Processing Method Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose Answering-Classifying-Correcting (ACC) framework, which employs a post-processing strategy to handle incorrect predictions. |
JIAYI LIN et. al. | arxiv-cs.CL | 2024-10-22 |
| 491 | Which Client Is Reliable?: A Reliable and Personalized Prompt-based Federated Learning for Medical Image Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a novel personalized federated learning (pFL) method for medical visual question answering (VQA) models, addressing privacy reliability challenges in the medical domain. |
He Zhu; Ren Togo; Takahiro Ogawa; Miki Haseyama; | arxiv-cs.CV | 2024-10-22 |
| 492 | VoiceTextBlender: Augmenting Large Language Models with Speech Capabilities Via Single-Stage Joint Speech-Text Supervised Fine-Tuning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Another critical challenge with SpeechLMs is catastrophic forgetting, where models optimized for speech tasks suffer significant degradation in text-only performance. To mitigate these issues, we propose a novel single-stage joint speech-text SFT approach on the low-rank adaptation (LoRA) of the LLM backbone. |
YIFAN PENG et. al. | arxiv-cs.CL | 2024-10-22 |
| 493 | Reasoning Before Responding: Towards Legal Long-form Question Answering with Interpretability Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The endeavor to generate detailed answers to contextually rich legal questions has faced challenges, primarily due to the limited availability of specialized datasets involving intensive manual effort or incapability of existing LFQA models to produce informative responses. Addressing this, our research introduces a semi-synthetic dataset, Legal-LFQA (L2FQA) created by exploiting a large language model (LLM) and utilizing contexts derived from existing legal datasets. |
Utkarsh Ujwal; Sai Sri Harsha Surampudi; Sayantan Mitra; Tulika Saha; | cikm | 2024-10-21 |
| 494 | Enhancing The Completeness of Rationales for Multi-Step Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, drawing inspiration from human-like reasoning processes in answering multi-step questions, we explicitly plan the rationales to ensure their completeness. |
SHANGZI XUE et. al. | cikm | 2024-10-21 |
| 495 | Optimal Query Allocation in Extractive QA with LLMs: A Learning-to-Defer Framework with Theoretical Guarantees Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a Learning-to-Defer framework that allocates queries to specialized experts, ensuring high-confidence predictions while optimizing computational efficiency. |
Yannis Montreuil; Shu Heng Yeo; Axel Carlier; Lai Xing Ng; Wei Tsang Ooi; | arxiv-cs.CL | 2024-10-21 |
| 496 | Fine-Tuning LLMs for Reliable Medical Question-Answering Services Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present an advanced approach to medical question-answering (QA) services, using fine-tuned Large Language Models (LLMs) to improve the accuracy and reliability of healthcare information. |
Ali Anaissi; Ali Braytee; Junaid Akram; | arxiv-cs.CL | 2024-10-21 |
| 497 | LeDQA: A Chinese Legal Case Document-based Question Answering Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present LeDQA, the first Chinese legal case document-based question answering dataset to our best knowledge. |
Bulou Liu; Zhenhao Zhu; Qingyao Ai; Yiqun Liu; Yueyue Wu; | cikm | 2024-10-21 |
| 498 | RD-P: A Trustworthy Retrieval-Augmented Prompter with Knowledge Graphs for LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel method called Retrieve-and-Discriminate Prompter (RD-P), which leverages knowledge graphs (KGs) for trustworthy RAG by synchronizing knowledge retrieval and discrimination in a unified model. |
Yubo Huang; Guosun Zeng; | cikm | 2024-10-21 |
| 499 | In Situ Answer Sentence Selection at Web-scale Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present Passage-based Extracting Answer Sentence In-place (PEASI), a novel answer selection model optimized for Web-scale setting. |
Zeyu Zhang; Thuy Vu; Alessandro Moschitti; | cikm | 2024-10-21 |
| 500 | Retrieval-enhanced Knowledge Editing in Language Models for Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To tackle the problem, we propose the Retrieval-Augmented model Editing (RAE) framework for multi-hop question answering. |
YUCHENG SHI et. al. | cikm | 2024-10-21 |
| 501 | Reverse Question Answering: Can An LLM Write A Question So Hard (or Bad) That It Can’t Answer? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: By finding question and answer types that lead to RQA errors, we suggest improvements for LLM reasoning. |
NISHANT BALEPUR et. al. | arxiv-cs.CL | 2024-10-20 |
| 502 | MedLogic-AQA: Enhancing Medical Question Answering with Abstractive Models Focusing on Logical Structures Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing approaches often struggle to grasp the intricate logical structures and relationships inherent in medical contexts, thus limiting their capacity to furnish precise and nuanced answers. In this work, we address this gap by proposing a novel Abstractive QA system MedLogic-AQA that harnesses First Order Logic (FOL) based rules extracted from both context and questions to generate well-grounded answers. |
Aizan Zafar; Kshitij Mishra; Asif Ekbal; | arxiv-cs.CL | 2024-10-20 |
| 503 | BRIEF: Bridging Retrieval and Inference for Multi-hop Reasoning Via Compression Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To accelerate inference, reduce costs, and minimize distractions, this paper presents BRIEF (Bridging Retrieval and Inference through Evidence Fusion), a lightweight approach that performs query-aware multi-hop reasoning by compressing retrieved documents into highly dense textual summaries to integrate into in-context learning. |
Yuankai Li; Jia-Chen Gu; Di Wu; Kai-Wei Chang; Nanyun Peng; | arxiv-cs.CL | 2024-10-20 |
| 504 | ChitroJera: A Regionally Relevant Visual Question Answering Dataset for Bangla Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, existing Bangla VQA datasets offer little cultural relevance and are largely adapted from their foreign counterparts. To address these challenges, we introduce a large-scale Bangla VQA dataset titled ChitroJera, totaling over 15k samples where diverse and locally relevant data sources are used. |
DEEPARGHYA DUTTA BARUA et. al. | arxiv-cs.CV | 2024-10-19 |
| 505 | Electrocardiogram-Language Model for Few-Shot Question Answering with Meta Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work introduces a novel multimodal meta-learning method for few-shot ECG question answering, addressing the challenge of limited labeled data while leveraging the rich knowledge encoded within large language models (LLMs). |
Jialu Tang; Tong Xia; Yuan Lu; Cecilia Mascolo; Aaqib Saeed; | arxiv-cs.LG | 2024-10-18 |
| 506 | Optimizing Retrieval-Augmented Generation with Elasticsearch for Enhanced Question-Answering Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study aims to improve the accuracy and quality of large-scale language models (LLMs) in answering questions by integrating Elasticsearch into the Retrieval Augmented Generation (RAG) framework. |
JIAJING CHEN et. al. | arxiv-cs.IR | 2024-10-18 |
| 507 | SwaQuAD-24: QA Benchmark Dataset in Swahili Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes the creation of a Swahili Question Answering (QA) benchmark dataset, aimed at addressing the underrepresentation of Swahili in natural language processing (NLP). |
Alfred Malengo Kondoro; | arxiv-cs.CL | 2024-10-18 |
| 508 | Modeling Future Conversation Turns to Teach LLMs to Ask Clarifying Questions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our method achieves a 5% improvement in F1 measured against the answer set from different interpretations of each query, showing the value of modeling future conversation turns. |
Michael J. Q. Zhang; W. Bradley Knox; Eunsol Choi; | arxiv-cs.CL | 2024-10-17 |
| 509 | BQA: Body Language Question Answering Dataset for Video Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Enabling current Video Large Language Models (VideoLLMs) to accurately interpret body language is a crucial challenge, as human unconscious actions can easily cause the model to misinterpret their intent. To address this, we propose a dataset, BQA, a body language question answering dataset, to validate whether the model can correctly interpret emotions from short clips of body language comprising 26 emotion labels of videos of body language. |
SHINTARO OZAKI et. al. | arxiv-cs.CL | 2024-10-17 |
| 510 | LEGAL-UQA: A Low-Resource Urdu-English Dataset for Legal Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present LEGAL-UQA, the first Urdu legal question-answering dataset derived from Pakistan’s constitution. |
Faizan Faisal; Umair Yousaf; | arxiv-cs.CL | 2024-10-16 |
| 511 | Open Domain Question Answering with Conflicting Contexts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To explore how humans reason through conflicting contexts, we request our annotators to provide explanations for their selections of correct answers. We demonstrate that by finetuning LLMs to explain their answers, we can introduce richer information into their training that guide them through the process of reasoning with conflicting contexts. |
SIYI LIU et. al. | arxiv-cs.CL | 2024-10-16 |
| 512 | AGENTiGraph: An Interactive Knowledge Graph Platform for LLM-based Chatbots Utilizing Private Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce AGENTiGraph (Adaptive Generative ENgine for Task-based Interaction and Graphical Representation), a platform for knowledge management through natural language interaction. |
XINJIE ZHAO et. al. | arxiv-cs.AI | 2024-10-15 |
| 513 | RuleRAG: Rule-Guided Retrieval-Augmented Generation with Language Models for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing approaches only consider the query itself, neither specifying the retrieval preferences for the retrievers nor informing the generators of how to refer to the retrieved documents for the answers, which poses a significant challenge to the QA performance. To address these issues, we propose Rule-guided Retrieval-Augmented Generation with LMs, which explicitly introduces rules for in-context learning (RuleRAG-ICL) to guide retrievers to recall related documents in the directions of rules and uniformly guide generators to reason attributed by the same rules. |
ZHONGWU CHEN et. al. | arxiv-cs.IR | 2024-10-15 |
| 514 | RuleRAG: Rule-guided Retrieval-augmented Generation with Language Models for Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Retrieval-augmented generation (RAG) framework has shown promising potential in knowledge-intensive question answering (QA) by retrieving external corpus and generating based on … |
ZHONGWU CHEN et. al. | ArXiv | 2024-10-15 |
| 515 | Eliminating The Language Bias for Visual Question Answering with Fine-grained Causal Intervention Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel causal intervention training scheme named CIBi to eliminate language bias from a finer-grained perspective. |
YING LIU et. al. | arxiv-cs.CV | 2024-10-14 |
| 516 | TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce TemporalBench, a new benchmark dedicated to evaluating fine-grained temporal understanding in videos. |
MU CAI et. al. | arxiv-cs.CV | 2024-10-14 |
| 517 | BanglaQuAD: A Bengali Open-domain Question Answering Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces BanglaQuAD, a Bengali question answering dataset, containing 30,808 question-answer pairs constructed from Bengali Wikipedia articles by native speakers. |
MD RASHAD AL HASAN RONY et. al. | arxiv-cs.CL | 2024-10-14 |
| 518 | Representing Charts As Text for Language Models: An In-Depth Study of Question Answering for Bar Charts Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Machine Learning models for chart-grounded Q&A (CQA) often treat charts as images, but performing CQA on pixel values has proven challenging. We thus investigate a resource … |
V. Bursztyn; J. Hoffswell; E. Koh; Shunan Guo; | 2024 IEEE Visualization and Visual Analytics (VIS) | 2024-10-13 |
| 519 | LoRE: Logit-Ranked Retriever Ensemble for Enhancing Open-Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose LoRE (Logit-Ranked Retriever Ensemble), a novel approach that improves answer accuracy and relevance by mitigating positional bias. |
Saikrishna Sanniboina; Shiv Trivedi; Sreenidhi Vijayaraghavan; | arxiv-cs.CL | 2024-10-13 |
| 520 | A Step Towards Mixture of Grader: Statistical Analysis of Existing Automatic Evaluation Metrics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As a potential solution, we discuss how a Mixture Of Grader could potentially improve the auto QA evaluator quality. |
Yun Joon Soh; Jishen Zhao; | arxiv-cs.CL | 2024-10-13 |
| 521 | Quebec Automobile Insurance Question-Answering With Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces two corpora: the Quebec Automobile Insurance Expertise Reference Corpus and a set of 82 Expert Answers to Layperson Automobile Insurance Questions. |
David Beauchemin; Zachary Gagnon; Ricahrd Khoury; | arxiv-cs.CL | 2024-10-12 |
| 522 | Declarative Knowledge Distillation from Large Language Models for Visual Question Answering Datasets Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The downside is that crafting the rules for such a component can be an additional burden on the developer. We address this challenge by presenting an approach for declarative knowledge distillation from Large Language Models (LLMs). |
Thomas Eiter; Jan Hadl; Nelson Higuera; Johannes Oetsch; | arxiv-cs.AI | 2024-10-12 |
| 523 | Continuous Risk Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Many prior approaches either rely on access to task identities during testing or fail to adequately model samples from unseen tasks, which limits their practical applicability. To overcome these limitations, we introduce Diana , a novel \underline{d}ynam\underline{i}c \underline{a}rchitecture-based lifelo\underline{n}g Q\underline{A} framework designed to learn a sequence of QA tasks using a prompt-enhanced language model.Diana leverages four hierarchically structured types of prompts to capture QA knowledge at multiple levels of granularity. |
Yi Dai; | arxiv-cs.CL | 2024-10-12 |
| 524 | Retrieving Contextual Information for Long-Form Question Answering Using Weak Supervision Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose and compare different weak supervision techniques to optimize retrieval for contextual information. |
Philipp Christmann; Svitlana Vakulenko; Ionut Teodor Sorodoc; Bill Byrne; Adrià de Gispert; | arxiv-cs.CL | 2024-10-11 |
| 525 | ACCEPT: Adaptive Codebook for Composite and Efficient Prompt Tuning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Traditionally, each prompt has been considered indivisible and updated independently, leading the parameters increase proportionally as prompt length grows. To address this issue, we propose Adaptive Codebook for Composite and Efficient Prompt Tuning (ACCEPT). |
Yu-Chen Lin; Wei-Hua Li; Jun-Cheng Chen; Chu-Song Chen; | arxiv-cs.CL | 2024-10-10 |
| 526 | FltLM: An Intergrated Long-Context Large Language Model for Effective Context Filtering and Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, Long-Context LLMs still face two critical challenges: The lost in the middle phenomenon, where crucial middle-context information is likely to be missed, and the distraction issue that the models lose focus due to overly extended contexts. To address these challenges, we propose the Context Filtering Language Model (FltLM), a novel integrated Long-Context LLM which enhances the ability of the model on multi-document question-answering (QA) tasks. |
JINGYANG DENG et. al. | arxiv-cs.CL | 2024-10-09 |
| 527 | QA-Calibration of Language Model Confidence Scores Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We argue, however, that this standard (average-case) notion of calibration is difficult to interpret for decision-making in generative QA. To address this, we generalize the standard notion of average calibration and introduce QA-calibration, which ensures calibration holds across different question-and-answer groups. |
Putra Manggala; Atalanti Mastakouri; Elke Kirschbaum; Shiva Prasad Kasiviswanathan; Aaditya Ramdas; | arxiv-cs.CL | 2024-10-09 |
| 528 | Do Great Minds Think Alike? Investigating Human-AI Complementarity in Question Answering with CAIMIRA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recent advancements of large language models (LLMs) have led to claims of AI surpassing humans in natural language processing (NLP) tasks such as textual understanding and reasoning. This work investigates these assertions by introducing CAIMIRA, a novel framework rooted in item response theory (IRT) that enables quantitative assessment and comparison of problem-solving abilities of question-answering (QA) agents: humans and AI systems. |
Maharshi Gor; Hal Daumé III; Tianyi Zhou; Jordan Boyd-Graber; | arxiv-cs.CL | 2024-10-08 |
| 529 | ActionAtlas: A VideoQA Benchmark for Domain-specialized Action Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Within any single domain, actions can often appear quite similar, making it challenging for deep models to distinguish them accurately. To evaluate the effectiveness of multimodal foundation models in helping us recognize such actions, we present ActionAtlas v1.0, a multiple-choice video question answering benchmark featuring short videos across various sports. |
MOHAMMADREZA SALEHI et. al. | arxiv-cs.CV | 2024-10-08 |
| 530 | PDF-WuKong: A Large Multimodal Model for Efficient Long PDF Reading with End-to-End Sparse Sampling Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce PDF-WuKong, a multimodal large language model (MLLM) which is designed to enhance multimodal question-answering (QA) for long PDF documents. |
XUDONG XIE et. al. | arxiv-cs.CV | 2024-10-08 |
| 531 | ERVQA: A Dataset to Benchmark The Readiness of Large Vision Language Models in Hospital Environments Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce the Emergency Room Visual Question Answering (ERVQA) dataset, consisting of |
SOURJYADIP RAY et. al. | arxiv-cs.CL | 2024-10-08 |
| 532 | Document-level Causal Relation Extraction with Knowledge-guided Binary Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Knowledge-guided binary Question Answering (KnowQA) method with event structures for ECRE, consisting of two stages: Event Structure Construction and Binary Question Answering. |
Zimu Wang; Lei Xia; Wei Wang; Xinya Du; | arxiv-cs.CL | 2024-10-07 |
| 533 | Cost-efficient Knowledge-based Question Answering with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose Coke, a novel cost-efficient strategy for KBQA with LLMs, modeled as a tailored multi-armed bandit problem to minimize calls to LLMs within limited budgets. |
JUNNAN DONG et. al. | nips | 2024-10-07 |
| 534 | Right This Way: Can VLMs Guide Us to See More to Answer Questions? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This capability is especially valuable for assisting visually impaired individuals. To evaluate this capability of current VLMs, we introduce a human-labeled dataset as a benchmark for this task. |
LI LIU et. al. | nips | 2024-10-07 |
| 535 | MEQA: A Benchmark for Multi-hop Event-centric Question Answering with Explanations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a novel semi-automatic question generation strategy by composing event structures from information extraction (IE) datasets and present the first Multi-hop Event-centric Question Answering (MEQA) benchmark. |
Ruosen Li; Zimu Wang; Son Tran; Lei Xia; Xinya Du; | nips | 2024-10-07 |
| 536 | CRAG – Comprehensive RAG Benchmark IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing RAG datasets, however, do not adequately represent the diverse and dynamic nature of real-world Question Answering (QA) tasks. To bridge this gap, we introduce the Comprehensive RAG Benchmark (CRAG), a factual question answering benchmark of 4,409 question-answer pairs and mock APIs to simulate web and Knowledge Graph (KG) search. |
XIAO YANG et. al. | nips | 2024-10-07 |
| 537 | FinBen: An Holistic Financial Benchmark for Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce FinBen, the first extensive open-source evaluation benchmark, including 36 datasets spanning 24 financial tasks, covering seven critical aspects: information extraction (IE), textual analysis, question answering (QA), text generation, risk management, forecasting, and decision-making. |
QIANQIAN XIE et. al. | nips | 2024-10-07 |
| 538 | CausalChaos! Dataset for Comprehensive Causal Action Question Answering Over Longer Causal Chains Grounded in Dynamic Visual Scenes Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We identify more advanced/explicit causal relationship modeling \& joint modeling of vision and language as the immediate areas for future efforts to focus upon. |
PARITOSH PARMAR et. al. | nips | 2024-10-07 |
| 539 | CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We benchmark several Multimodal Large Language Models (MLLMs) on CVQA, and we show that the dataset is challenging for the current state-of-the-art models. |
DAVID ROMERO et. al. | nips | 2024-10-07 |
| 540 | Crafting Interpretable Embeddings By Asking LLMs Questions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce question-answering embeddings (QA-Emb), embeddings where each feature represents an answer to a yes/no question asked to an LLM. |
VINAMRA BENARA et. al. | nips | 2024-10-07 |
| 541 | RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To foster sound evaluation of language models, we introduce a new test dataset named RepLiQA, suited for question-answering and topic retrieval tasks. |
JOAO MONTEIRO et. al. | nips | 2024-10-07 |
| 542 | LongVideoBench: A Benchmark for Long-context Interleaved Video-Language Understanding IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Albeit the progress, few public benchmark is available to measure such development. To mitigate this gap, we introduce LongVideoBench, a question-answering benchmark that features video-language interleaved inputs up to an hour long. |
Haoning Wu; DONGXU LI; Bei Chen; Junnan Li; | nips | 2024-10-07 |
| 543 | LOVA3: Learning to Visual Question Answering, Asking and Assessment Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, current Multimodal Large Language Models (MLLMs) primarily focus on question answering, often neglecting the full potential of questioning and assessment skills. In this study, we introduce LOVA3, an innovative framework named “Learning tO Visual Question Answering, Asking and Assessment,” designed to equip MLLMs with these additional capabilities. |
Hengyuan Zhao; Pan Zhou; Difei Gao; Mike Zheng Shou; | nips | 2024-10-07 |
| 544 | G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In contrast, we develop a flexible question-answering framework targeting real-world textual graphs, applicable to multiple applications including scene graph understanding, common sense reasoning, and knowledge graph reasoning. |
XIAOXIN HE et. al. | nips | 2024-10-07 |
| 545 | FAMMA: A Benchmark for Financial Domain Multilingual Multimodal Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce FAMMA, an open-source benchmark for \underline{f}in\underline{a}ncial \underline{m}ultilingual \underline{m}ultimodal question \underline{a}nswering (QA). |
SIQIAO XUE et. al. | arxiv-cs.CL | 2024-10-06 |
| 546 | Optimizing AI Reasoning: A Hamiltonian Dynamics Approach to Multi-Hop Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: This paper introduces an innovative approach to analyzing and improving multi-hop reasoning in AI systems by drawing inspiration from Hamiltonian mechanics. We propose a novel … |
Javier Marin; | ArXiv | 2024-10-06 |
| 547 | Geometric Analysis of Reasoning Trajectories: A Phase Space Approach to Understanding Valid and Invalid Multi-Hop Reasoning in LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a novel approach to analyzing multi-hop reasoning in language models through Hamiltonian mechanics. |
Javier Marin; | arxiv-cs.AI | 2024-10-06 |
| 548 | Adaptive Question Answering: Enhancing Language Model Proficiency for Addressing Knowledge Conflicts with Source Citations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the importance of both aspects, no prior research has combined them, leaving a significant gap in the development of QA systems. In this work, we bridge this gap by proposing the novel task of QA with source citation in ambiguous settings, where multiple valid answers exist. |
Sagi Shaier; Ari Kobren; Philip Ogren; | arxiv-cs.CL | 2024-10-05 |
| 549 | Cross-lingual Transfer for Automatic Question Generation By Learning Interrogative Structures in Target Languages Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a simple and efficient XLT-QG method that operates without the need for monolingual, parallel, or labeled data in the target language, utilizing a small language model. |
Seonjeong Hwang; Yunsu Kim; Gary Geunbae Lee; | arxiv-cs.CL | 2024-10-04 |
| 550 | Question-Answering System for Bangla: Fine-tuning BERT-Bangla for A Closed Domain Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Question-answering systems for Bengali have seen limited development, particularly in domain-specific applications. Leveraging advancements in natural language processing, this paper explores a fine-tuned BERT-Bangla model to address this gap. |
Subal Chandra Roy; Md Motaleb Hossen Manik; | arxiv-cs.CL | 2024-10-04 |
| 551 | Structured List-Grounded Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Motivated by the observation that even advanced language models like GPT-3.5 often miss semantic cues from lists, this paper aims to enhance question answering (QA) systems for better interpretation and use of structured lists. |
MUJEEN SUNG et. al. | arxiv-cs.CL | 2024-10-04 |
| 552 | ALR2: A Retrieve-then-Reason Framework for Long-context Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The context window of large language models (LLMs) has been extended significantly in recent years. However, while the context length that the LLM can process has grown, the … |
HUAYANG LI et. al. | ArXiv | 2024-10-04 |
| 553 | ALR$^2$: A Retrieve-then-Reason Framework for Long-context Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We find that modern LLMs struggle to accurately retrieve relevant facts and instead, often hallucinate retrieved facts, resulting in flawed reasoning and the production of incorrect answers. To address these issues, we introduce ALR$^2$, a method that augments the long-context reasoning capability of LLMs via an explicit two-stage procedure, i.e., aligning LLMs with the objectives of both retrieval and reasoning. |
HUAYANG LI et. al. | arxiv-cs.CL | 2024-10-04 |
| 554 | Video Instruction Tuning With Synthetic Data IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The development of video large multimodal models (LMMs) has been hindered by the difficulty of curating large amounts of high-quality raw data from the web. To address this, we propose an alternative approach by creating a high-quality synthetic dataset specifically for video instruction-following, namely LLaVA-Video-178K. |
YUANHAN ZHANG et. al. | arxiv-cs.CV | 2024-10-03 |
| 555 | Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce SMART-SLIC, a highly domain-specific LLM framework, that integrates RAG with KG and a vector store (VS) that store factual domain specific information. |
RYAN C. BARRON et. al. | arxiv-cs.CL | 2024-10-03 |
| 556 | Coal Mining Question Answering with LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a novel approach to coal mining question answering (QA) using large language models (LLMs) combined with tailored prompt engineering techniques. |
Antonio Carlos Rivera; Anthony Moore; Steven Robinson; | arxiv-cs.CL | 2024-10-03 |
| 557 | Listening to The Wise Few: Select-and-Copy Attention Heads for Multiple-Choice QA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, such a format for evaluating LLMs has limitations, since even if the model knows the correct answer, it may struggle to select the corresponding letter simply due to difficulties in following this rigid format. To address this, we introduce new scores that better capture and reveal model’s underlying knowledge: the Query-Key Score (QK-score), derived from the interaction between query and key representations in attention heads, and the Attention Score, based on attention weights. |
EDUARD TULCHINSKII et. al. | arxiv-cs.CL | 2024-10-03 |
| 558 | Question-guided Knowledge Graph Re-scoring and Injection for Knowledge Graph Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the retrieved subgraph inevitably brings distraction information for knowledge utilization, impeding the model’s ability to perform accurate reasoning. To address this issue, we propose a Question-guided Knowledge Graph Re-scoring method (Q-KGR) to eliminate noisy pathways for the input question, thereby focusing specifically on pertinent factual knowledge. |
YU ZHANG et. al. | arxiv-cs.CL | 2024-10-02 |
| 559 | AHP-Powered LLM Reasoning for Multi-Criteria Evaluation of Open-Ended Responses Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we propose a method that leverages LLMs and the analytic hierarchy process (AHP) to assess answers to open-ended questions. |
Xiaotian Lu; Jiyi Li; Koh Takeuchi; Hisashi Kashima; | arxiv-cs.CL | 2024-10-02 |
| 560 | Robust Visual Question Answering Utilizing Bias Instances and Label Imbalance Related Papers Related Patents Related Grants Related Venues Related Experts View |
Liang Zhao; Kefeng Li; Jiangtao Qi; Yanhan Sun; Zhenfang Zhu; | Knowl. Based Syst. | 2024-10-01 |
| 561 | Benchmarking Large Language Models for Conversational Question Answering in Multi-instructional Documents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing benchmarks have primarily focused on basic factual question-answering from single narrative documents, making them inadequate for assessing a model`s ability to comprehend complex real-world instructional documents and provide accurate step-by-step guidance in daily life. To bridge this gap, we present InsCoQA, a novel benchmark tailored for evaluating large language models (LLMs) in the context of CQA with instructional documents. |
SHIWEI WU et. al. | arxiv-cs.CL | 2024-10-01 |
| 562 | AgXQA: A Benchmark for Advanced Agricultural Extension Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
Josué Kpodo; Parisa Kordjamshidi; A. P. Nejadhashemi; | Comput. Electron. Agric. | 2024-10-01 |
| 563 | Quantifying Reliance on External Information Over Parametric Knowledge During Retrieval Augmented Generation (RAG) Using Mechanistic Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose (a) Causal Mediation Analysis; for proving that parametric memory is minimally utilized when answering a question and (b) Attention Contributions and Knockouts for showing the last token residual stream do not get enriched from the subject token in the question, but gets enriched from tokens of RAG-context. |
RESHMI GHOSH et. al. | arxiv-cs.CL | 2024-10-01 |
| 564 | Video Question Answering with Procedural Programs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose to answer questions about videos by generating short procedural programs that solve visual subtasks to obtain a final answer. |
Rohan Choudhury; Koichiro Niinuma; Kris Kitani; Laszlo A Jeni; | eccv | 2024-09-30 |
| 565 | WSI-VQA: Interpreting Whole Slide Images By Generative Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel framework (WSI-VQA) to interpret WSIs by generative visual question answering. |
Pingyi Chen; Chenglu Zhu; Sunyi Zheng; Honglin Li; Lin Yang; | eccv | 2024-09-30 |
| 566 | Vamos: Versatile Action Models for Video Understanding IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose versatile action models (Vamos), a learning framework powered by a large language model as the “reasoner”, and can flexibly leverage visual embedding and free-form text descriptions as its input. |
SHIJIE WANG et. al. | eccv | 2024-09-30 |
| 567 | Compositional Substitutivity of Visual Reasoning for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we explore the compositional substitutivity of visual reasoning in the context of visual question answering (VQA).Specifically, for each question-image pair, we construct a support question set and a support image set, and both sets contain questions/images that share synonymous primitives with the original question/image.To quantitatively evaluate the substitutivity of VQA models, we introduce two datasets: GQA-SPS and VQA-SPS v2, by performing three types of substitutions using synonymous primitives including words, visual entities, and referents. |
CHUANHAO LI et. al. | eccv | 2024-09-30 |
| 568 | FunQA: Towards Surprising Video Comprehension IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce FunQA, a challenging video question answering (QA) dataset specifically designed to evaluate and enhance the depth of video reasoning based on counter-intuitive and fun videos. |
BINZHU XIE et. al. | eccv | 2024-09-30 |
| 569 | LingoQA: Video Question Answering for Autonomous Driving Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce LingoQA, a novel dataset and benchmark for visual question answering in autonomous driving.We release our dataset and benchmark1 as an evaluation platform for vision-language models in autonomous driving. |
ANA-MARIA MARCU et. al. | eccv | 2024-09-30 |
| 570 | QAEncoder: Towards Aligned Representation Learning in Question Answering System Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the inherent gap between user queries and relevant documents hinders precise matching. Motivated by our conical distribution hypothesis, which posits that potential queries and documents form a cone-like structure in the embedding space, we introduce QAEncoder, a training-free approach to bridge this gap. |
ZHENGREN WANG et. al. | arxiv-cs.CL | 2024-09-30 |
| 571 | VideoINSTA: Zero-shot Long Video Understanding Via Informative Spatial-Temporal Reasoning with LLMs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a framework VideoINSTA, i.e. INformative Spatial-TemporAl Reasoning for zero-shot long-form video understanding. |
RUOTONG LIAO et. al. | arxiv-cs.CV | 2024-09-30 |
| 572 | ViLA: Efficient Video-Language Alignment for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose an efficient Video-Language Alignment (ViLA) network. |
XIJUN WANG et. al. | eccv | 2024-09-30 |
| 573 | TimeCraft: Navigate Weakly-Supervised Temporal Grounded Video Question Answering Via Bi-directional Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on the grounded VQA task, which necessitates models to provide answers along with explicit visual evidence, i.e., certain video segments. |
Huabin Liu; Xiao Ma; Cheng Zhong; Yang Zhang; Weiyao Lin; | eccv | 2024-09-30 |
| 574 | DriveLM: Driving with Graph Visual Question Answering IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We instantiate datasets (DriveLM-Data) built upon nuScenes and CARLA, and propose a VLM-based baseline approach (DriveLM-Agent) for jointly performing Graph VQA and end-to-end driving. |
CHONGHAO SIMA et. al. | eccv | 2024-09-30 |
| 575 | An Explainable Vision Question Answer Model Via Diffusion Chain-of-Thought Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This means that generating explanations solely for the answer can lead to a semantic discrepancy between the content of the explanation and the question-answering content. To address this, we propose a step-by-step reasoning approach to reduce such semantic discrepancies. |
Chunhao LU; Qiang Lu; Jake Luo; | eccv | 2024-09-30 |
| 576 | Q&A Prompts: Discovering Rich Visual Clues Through Mining Question-Answer Prompts for VQA Requiring Diverse World Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we believe that if we can collect rich visual clues, we will recognize the image more accurately, understand the question better, recall relevant knowledge more easily, and finally reason out the answer. |
Haibo Wang; Weifeng Ge; | eccv | 2024-09-30 |
| 577 | AutoEval-Video: An Automatic Benchmark for Assessing Large Vision Language Models in Open-Ended Video Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a novel and challenging benchmark, AutoEval-Video, to comprehensively evaluate large vision-language models in open-ended video question answering. |
Weiran Huang; Xiuyuan Chen; Yuan Lin; Yuchen Zhang; | eccv | 2024-09-30 |
| 578 | Fully Authentic Visual Question Answering Dataset from Online Communities Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce the first VQA dataset in which all contents originate from an authentic use case. |
CHONGYAN CHEN et. al. | eccv | 2024-09-30 |
| 579 | Towards Robust Extractive Question Answering Models: Rethinking The Training Methodology Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes a novel training method to improve the robustness of Extractive Question Answering (EQA) models. |
Son Quoc Tran; Matt Kretchmar; | arxiv-cs.CL | 2024-09-29 |
| 580 | CoTKR: Chain-of-Thought Enhanced Knowledge Rewriting for Complex Knowledge Graph Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address them, we propose a novel rewriting method CoTKR, Chain-of-Thought Enhanced Knowledge Rewriting, for generating reasoning traces and corresponding knowledge in an interleaved manner, thereby mitigating the limitations of single-step knowledge rewriting. |
YIKE WU et. al. | arxiv-cs.CL | 2024-09-29 |
| 581 | See Then Tell: Enhancing Key Information Extraction with Vision Grounding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce STNet (See then Tell Net), a novel end-to-end model designed to deliver precise answers with relevant vision grounding. |
SHUHANG LIU et. al. | arxiv-cs.CV | 2024-09-29 |
| 582 | Zero-Shot Multi-Hop Question Answering Via Monte-Carlo Tree Search with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unlike previous works, we propose a zero-shot prompting method, which relies solely on instructions without the support of hand-crafted few-shot examples that typically require domain expertise. |
SEONGMIN LEE et. al. | arxiv-cs.CL | 2024-09-28 |
| 583 | A Fine-tuned Multimodal Large Model for Power Defect Image-text Question-answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
QIQI WANG et. al. | Signal Image Video Process. | 2024-09-28 |
| 584 | Exploring Language Model Generalization in Low-Resource Extractive QA Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we investigate Extractive Question Answering (EQA) with Large Language Models (LLMs) under domain drift, i.e., can LLMs generalize to domains that require specific knowledge such as medicine and law in a zero-shot fashion without additional in-domain training? |
Saptarshi Sengupta; Wenpeng Yin; Preslav Nakov; Shreya Ghosh; Suhang Wang; | arxiv-cs.CL | 2024-09-27 |
| 585 | Rehearsing Answers to Probable Questions with Perspective-Taking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, scenarios involving the preparation of answers to probable questions during professional oral presentations remain underexplored. In this paper, we pioneer the examination of this crucial yet overlooked topic by utilizing real-world QA conversation transcripts between company managers and professional analysts. |
Yung-Yu Shih; Ziwei Xu; Hiroya Takamura; Yun-Nung Chen; Chung-Chi Chen; | arxiv-cs.CL | 2024-09-27 |
| 586 | Efficient In-Domain Question Answering for Resource-Constrained Environments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we combine RAFT with LoRA to reduce fine tuning and storage requirements and gain faster inference times while maintaining comparable RAG performance. |
Isaac Chung; Phat Vo; Arman C. Kizilkale; Aaron Reite; | arxiv-cs.CL | 2024-09-26 |
| 587 | SynTQA: Synergistic Table-based Question Answering Via Mixture of Text-to-SQL and E2E TQA Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To combine both strengths, we propose a Synergistic Table-based Question Answering approach that integrate different models via answer selection, which is agnostic to any model types. |
Siyue Zhang; Anh Tuan Luu; Chen Zhao; | arxiv-cs.CL | 2024-09-25 |
| 588 | Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel framework that enhances temporal awareness and reasoning through Temporal Information-Aware Embedding and Granular Contrastive Reinforcement Learning. |
Wanqi Yang; Yanda Li; Meng Fang; Ling Chen; | arxiv-cs.CL | 2024-09-25 |
| 589 | Detecting Temporal Ambiguity in Questions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a novel approach by using diverse search strategies based on disambiguated versions of the questions. |
Bhawna Piryani; Abdelrahman Abdallah; Jamshid Mozafari; Adam Jatowt; | arxiv-cs.CL | 2024-09-25 |
| 590 | 60 Data Points Are Sufficient to Fine-Tune LLMs for Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, effective strategies for fine-tuning LLMs for the QA task remain largely unexplored. To address this gap, we categorize supervised fine-tuning (SFT) data based on the extent of knowledge memorized by the pretrained LLMs and conduct a series of empirical analyses. |
JUNJIE YE et. al. | arxiv-cs.CL | 2024-09-24 |
| 591 | Exploring Hint Generation Approaches in Open-Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce a novel context preparation approach called HINTQA, which employs Automatic Hint Generation (HG) techniques. |
Jamshid Mozafari; Abdelrahman Abdallah; Bhawna Piryani; Adam Jatowt; | arxiv-cs.CL | 2024-09-24 |
| 592 | Unlocking Markets: A Multilingual Benchmark to Cross-Market Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce a large-scale dataset comprising over 7 million questions from 17 marketplaces across 11 languages. |
Yifei Yuan; Yang Deng; Anders Søgaard; Mohammad Aliannejadi; | arxiv-cs.CL | 2024-09-24 |
| 593 | Using Similarity to Evaluate Factual Consistency in Summaries Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Therefore, many techniques for detecting factual inconsistencies build pipelines around natural language inference (NLI) or question-answering (QA) models with additional supervised learning steps. In this paper, we revisit similarity-based metrics, showing that this failure stems from the comparison text selection and its granularity. |
Yuxuan Ye; Edwin Simpson; Raul Santos Rodriguez; | arxiv-cs.CL | 2024-09-23 |
| 594 | LINKAGE: Listwise Ranking Among Varied-Quality References for Non-Factoid QA Evaluation Via LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by the evolution from pointwise to pairwise to listwise in learning-to-rank methods, we propose a novel listwise NFQA evaluation approach, that utilizes LLMs to rank candidate answers in a list of reference answers sorted by descending quality. |
Sihui Yang; Keping Bi; Wanqing Cui; Jiafeng Guo; Xueqi Cheng; | arxiv-cs.CL | 2024-09-23 |
| 595 | Pareto-Optimized Open-Source LLMs for Healthcare Via Context Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: By advancing retrieval techniques and QA evaluation, we enable more affordable and reliable LLM solutions for healthcare. |
Jordi Bayarri-Planas; Ashwin Kumar Gururajan; Dario Garcia-Gasulla; | arxiv-cs.AI | 2024-09-23 |
| 596 | Learning When to Retrieve, What to Rewrite, and How to Respond in Conversational QA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a method for enabling LLMs to decide when to retrieve in RAG settings given a conversational context. |
Nirmal Roy; Leonardo F. R. Ribeiro; Rexhina Blloshmi; Kevin Small; | arxiv-cs.CL | 2024-09-23 |
| 597 | Scene-Text Grounding for Text-Based Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose to study Grounded TextVideoQA by forcing models to answer questions and spatio-temporally localize the relevant scene-text regions, thus decoupling QA from scenetext recognition and promoting research towards interpretable QA. |
SHENG ZHOU et. al. | arxiv-cs.CV | 2024-09-22 |
| 598 | QMOS: Enhancing LLMs for Telecommunication with Question Masked Loss and Option Shuffling Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces QMOS, an innovative approach which uses a Question-Masked loss and Option Shuffling trick to enhance the performance of LLMs in answering Multiple-Choice Questions in the telecommunications domain. |
Blessed Guda; Gabrial Zencha Ashungafac; Lawrence Francis; Carlee Joe-Wong; | arxiv-cs.CL | 2024-09-21 |
| 599 | SMART-RAG: Selection Using Determinantal Matrices for Augmented Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This issue is particularly evident in unsupervised retrieval settings, where there are no mechanisms to effectively mitigate these problems, leading to suboptimal context selection. To address this, we propose Selection using Matrices for Augmented Retrieval (SMART) in question answering tasks, a fully unsupervised and training-free framework designed to optimize context selection in RAG. |
Jiatao Li; Xinyu Hu; Xiaojun Wan; | arxiv-cs.CL | 2024-09-20 |
| 600 | First Place Solution to The Multiple-choice Video QA Track of The Second Perception Test Challenge Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this report, we present our first-place solution to the Multiple-choice Video Question Answering (QA) track of The Second Perception Test Challenge. |
YINGZHE PENG et. al. | arxiv-cs.CV | 2024-09-20 |
| 601 | AQA: Adaptive Question Answering in A Society of LLMs Via Contextual Multi-Armed Bandit Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this aim, we build on recent advances in the orchestration of multiple large language models (LLMs) and formulate adaptive QA as a dynamic orchestration challenge. We define this as a contextual multi-armed bandit problem, where the context is defined by the characteristics of the incoming question and the action space consists of potential communication graph configurations among the LLM agents. |
Mohanna Hoveyda; Arjen P. de Vries; Maarten de Rijke; Harrie Oosterhuis; Faegheh Hasibi; | arxiv-cs.CL | 2024-09-20 |
| 602 | A Multimodal Dense Retrieval Approach for Speech-Based Open-Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, the ASR model propagates its errors to the retriever. In this work, we try to alleviate these limitations by proposing an ASR-free, end-to-end trained multimodal dense retriever that can work directly on spoken questions. |
Georgios Sidiropoulos; Evangelos Kanoulas; | arxiv-cs.CL | 2024-09-20 |
| 603 | Evaluating Image Hallucination in Text-to-Image Generation with Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we focus on the problem of image hallucination, where images created by generation models fail to faithfully depict factual content. |
Youngsun Lim; Hojun Choi; Hyunjung Shim; | arxiv-cs.CV | 2024-09-19 |
| 604 | MQA-KEAL: Multi-hop Question Answering Under Knowledge Editing for Arabic Language Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although, there have been numerous attempts for LLMs Knowledge Editing (KE), i.e., to edit the LLMs prior knowledge and in turn test it via Multi-hop Question Answering (MQA), yet so far these studies are primarily focused on English language. To bridge this gap, in this paper we propose: Multi-hop Questioning Answering under Knowledge Editing for Arabic Language (MQA-KEAL). |
Muhammad Asif Ali; Nawal Daftardar; Mutayyaba Waheed; Jianbin Qin; Di Wang; | arxiv-cs.CL | 2024-09-18 |
| 605 | Uncertainty-Guided Self-Questioning and Answering for Video-Language Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Take video question answering (VideoQA) tasks as an example, human annotated questions and answers often cover only part of the video, since the corresponding text is often short and monotonous, leading to underutilization of video. To address this, we propose a Bootstrapping Video-Language Alignment framework (BoViLA), a self-training method that augments question samples during training process through LLM-based self-questioning and answering, which help model exploit video information and the internal knowledge of LLMs more thoroughly to improve modality alignment. |
JIN CHEN et. al. | arxiv-cs.CV | 2024-09-17 |
| 606 | ProSLM : A Prolog Synergized Language Model for Explainable Domain Specific Knowledge Based Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose \systemname{}, a novel neurosymbolic framework, to improve the robustness and reliability of LLMs in question-answering tasks. |
Priyesh Vakharia; Abigail Kufeldt; Max Meyers; Ian Lane; Leilani Gilpin; | arxiv-cs.CL | 2024-09-17 |
| 607 | Contextual Breach: Assessing The Robustness of Transformer-based QA Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a unique dataset that incorporates seven distinct types of adversarial noise into the context, each applied at five different intensity levels on the SQuAD dataset. |
Asir Saadat; Nahian Ibn Asad; Md Farhan Ishmam; | arxiv-cs.CL | 2024-09-17 |
| 608 | HALO: Hallucination Analysis and Learning Optimization to Empower LLMs with Retrieval-Augmented Context for Guided Clinical Decision Making Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces HALO, a novel framework designed to enhance the accuracy and reliability of medical question-answering (QA) systems by focusing on the detection and mitigation of hallucinations. |
SUMERA ANJUM et. al. | arxiv-cs.CL | 2024-09-16 |
| 609 | A Benchmark Dataset with Larger Context for Non-Factoid Question Answering Over Islamic Text Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Yet, the scarcity of QA systems tailored specifically to the detailed nature of inquiries about the Quranic Tafsir (explanation, interpretation, context of Quran for clarity) and Ahadith poses significant challenges. To address this gap, we introduce a comprehensive dataset meticulously crafted for QA purposes within the domain of Quranic Tafsir and Ahadith. |
Faiza Qamar; Seemab Latif; Rabia Latif; | arxiv-cs.CL | 2024-09-15 |
| 610 | QTG-VQA: Question-Type-Guided Architectural for VideoQA Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Particularly, considering the significant variation in dependency on temporal information across different question types, and given that the representation of such information coincidentally represents a principal challenge and difficulty for VideoQA as opposed to ImageQA. To address these challenges, we propose QTG-VQA, a novel architecture that incorporates question-type-guided attention and adaptive learning mechanism. |
Zhixian He; Pengcheng Zhao; Fuwei Zhang; Shujin Lin; | arxiv-cs.CV | 2024-09-14 |
| 611 | Contri(e)ve: Context + Retrieve for Scholarly Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a two step solution using open source Large Language Model(LLM): Llama3.1 for Scholarly-QALD dataset. |
Kanchan Shivashankar; Nadine Steinmetz; | arxiv-cs.IR | 2024-09-13 |
| 612 | Electrocardiogram Report Generation and Question Answering Via Retrieval-Augmented Self-Supervised Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Interpreting electrocardiograms (ECGs) and generating comprehensive reports remain challenging tasks in cardiology, often requiring specialized expertise and significant time investment. To address these critical issues, we propose ECG-ReGen, a retrieval-based approach for ECG-to-text report generation and question answering. |
Jialu Tang; Tong Xia; Yuan Lu; Cecilia Mascolo; Aaqib Saeed; | arxiv-cs.LG | 2024-09-13 |
| 613 | QueryCAD: Grounded Question Answering for CAD Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these models are rarely considered in novel AI-based approaches, such as the automatic synthesis of robot programs, as there are no readily available methods that would allow CAD models to be incorporated for the analysis, interpretation, or extraction of information. To address these limitations, we propose QueryCAD, the first system designed for CAD question answering, enabling the extraction of precise information from CAD models using natural language queries. |
Claudius Kienle; Benjamin Alt; Darko Katic; Rainer Jäkel; Jan Peters; | arxiv-cs.RO | 2024-09-13 |
| 614 | L3Cube-IndicQuest: A Benchmark Question Answering Dataset for Evaluating Knowledge of LLMs in Indic Context Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present the L3Cube-IndicQuest, a gold-standard factual question-answering benchmark dataset designed to evaluate how well multilingual LLMs capture regional knowledge across various Indic languages. |
Pritika Rohera; Chaitrali Ginimav; Akanksha Salunke; Gayatri Sawant; Raviraj Joshi; | arxiv-cs.CL | 2024-09-13 |
| 615 | Source2Synth: Synthetic Data Generation and Curation Grounded in Real Data Sources Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Source2Synth: a new method that can be used for teaching LLMs new skills without relying on costly human annotations. |
ALISIA LUPIDI et. al. | arxiv-cs.CL | 2024-09-12 |
| 616 | Multi-object Event Graph Representation Learning for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While prior works have focused on modeling individual object movements using transformer-based methods, they falter when capturing complex scenarios involving multiple objects (e.g., a boy is throwing a ball in a hoop). We propose a contrastive language event graph representation learning method called CLanG to address this limitation. |
Yanan Wang; Shuichiro Haruta; Donghuo Zeng; Julio Vizcarra; Mori Kurokawa; | arxiv-cs.CV | 2024-09-12 |
| 617 | Experimenting with Legal AI Solutions: The Case of Question-Answering for Access to Justice Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a human-centric legal NLP pipeline, covering data sourcing, inference, and evaluation. |
Jonathan Li; Rohan Bhambhoria; Samuel Dahan; Xiaodan Zhu; | arxiv-cs.CL | 2024-09-11 |
| 618 | Learning to Compress Contexts for Efficient Knowledge-based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Previous works like Retrival-Augmented VQA-v2 (RAVQA-v2) focus on utilizing as much input information, such as image-based textual descriptions and retrieved knowledge, as possible to improve performance, but they all overlook the issue that with the number of input tokens increasing, inference efficiency significantly decreases, which contradicts the demands of practical applications. To address this issue, we propose \textbf{R}etrieval-\textbf{A}ugmented MLLMs with Compressed Contexts (RACC). |
WEIXI WENG et. al. | arxiv-cs.CV | 2024-09-11 |
| 619 | Integrating SPARQL and LLMs for Question Answering Over Scholarly Data Sources Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper describes a methodology that combines SPARQL queries, divide and conquer algorithms, and a pre-trained extractive question answering model. |
Fomubad Borista Fondi; Azanzi Jiomekong Fidel; Gaoussou Camara; | arxiv-cs.IR | 2024-09-11 |
| 620 | QA-RAG: Exploring LLM Reliance on External Knowledge Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Large language models (LLMs) can store factual knowledge within their parameters and have achieved superior results in question-answering tasks. However, challenges persist in … |
Aigerim Mansurova; Aiganym Mansurova; A. Nugumanova; | Big Data Cogn. Comput. | 2024-09-09 |
| 621 | Seek and Solve Reasoning for Table Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper reveals that the reasoning process during task simplification may be more valuable than the simplified tasks themselves and aims to improve TQA performance by leveraging LLMs’ reasoning capabilities. We propose a Seek-and-Solve pipeline that instructs the LLM to first seek relevant information and then answer questions, integrating these two stages at the reasoning level into a coherent Seek-and-Solve Chain of Thought (SS-CoT). |
Ruya Jiang; Chun Wang; Weihong Deng; | arxiv-cs.CL | 2024-09-08 |
| 622 | Question-Answering Dense Video Events Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For improvement, we propose DeVi, a novel training-free MLLM approach that highlights a hierarchical captioning module, a temporal event memory module, and a self-consistency checking module to respectively detect, contextualize and memorize, and ground dense-events in long videos for question answering. |
Hangyu Qin; Junbin Xiao; Angela Yao; | arxiv-cs.CV | 2024-09-06 |
| 623 | Combining LLMs and Knowledge Graphs to Reduce Hallucinations in Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: A key issue is the hallucination problem, where models generate information unsupported by the underlying data, potentially leading to dangerous misinformation. This paper presents a novel approach designed to bridge this gap by combining Large Language Models (LLM) and Knowledge Graphs (KG) to improve the accuracy and reliability of question-answering systems, on the example of a biomedical KG. |
Larissa Pusch; Tim O. F. Conrad; | arxiv-cs.CL | 2024-09-06 |
| 624 | WebQuest: A Benchmark for Multimodal QA on Web Page Sequences Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present WebQuest, a multi-page question-answering dataset that requires reasoning across multiple related web pages. |
MARIA WANG et. al. | arxiv-cs.IR | 2024-09-06 |
| 625 | COLUMBUS: Evaluating COgnitive Lateral Understanding Through Multiple-choice ReBUSes Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Effective problem-solving also necessitates lateral thinking, which remains understudied in AI and has not been used to test visual perception systems. To bridge this gap, we formulate visual lateral thinking as a multiple-choice question-answering task and describe a three-step taxonomy-driven methodology for instantiating task examples. |
Koen Kraaijveld; Yifan Jiang; Kaixin Ma; Filip Ilievski; | arxiv-cs.CV | 2024-09-06 |
| 626 | RAG Based Question-Answering for Contextual Response Prediction System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce an end-to-end framework that employs LLMs with RAG capabilities for industry use cases. |
Sriram Veturi; Saurabh Vaichal; Reshma Lal Jagadheesh; Nafis Irtiza Tripto; Nian Yan; | arxiv-cs.CL | 2024-09-05 |
| 627 | GoT-CQA: Graph-of-Thought Guided Compositional Reasoning for Chart Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The former refers to answering this question strictly based on the analysis of the visual content or internal data of the given chart, while the latter emphasizes the various logical and numerical reasoning involved in answer prediction process. In this paper, we pay more attention on the complex reasoning in CQA task, and propose a novel Graph-of-Thought (GoT) guided compositional reasoning model called GoT-CQA to overcome this problem. |
LINGLING ZHANG et. al. | arxiv-cs.CV | 2024-09-04 |
| 628 | LongCite: Enabling LLMs to Generate Fine-grained Citations in Long-context QA IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we aim to enable long-context LLMs to generate responses with fine-grained sentence-level citations, improving their faithfulness and verifiability. |
JIAJIE ZHANG et. al. | arxiv-cs.CL | 2024-09-04 |
| 629 | MARAGS: A Multi-Adapter System for Multi-Task Retrieval Augmented Generation Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we present a multi-adapter retrieval augmented generation system (MARAGS) for Meta’s Comprehensive RAG (CRAG) competition for KDD CUP 2024. |
Mitchell DeHaven; | arxiv-cs.CL | 2024-09-04 |
| 630 | CRAFT Your Dataset: Task-Specific Synthetic Dataset Generation Through Corpus Retrieval and Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose Corpus Retrieval and Augmentation for Fine-Tuning (CRAFT), a method for generating synthetic datasets, given a small number of user-written few-shots that demonstrate the task to be performed. |
Ingo Ziegler; Abdullatif Köksal; Desmond Elliott; Hinrich Schütze; | arxiv-cs.CL | 2024-09-03 |
| 631 | Diversify-verify-adapt: Efficient and Robust Retrieval-Augmented Ambiguous Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although the iterative RAG approach has been proposed to address this problem, it comes at the cost of significantly reduced efficiency. To address these issues, we propose the diversify-verify-adapt (DIVA) framework. |
YEONJUN IN et. al. | arxiv-cs.CL | 2024-09-03 |
| 632 | Multi-modal Situated Reasoning in 3D Scenes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing datasets and benchmarks for situated understanding are limited in data modality, diversity, scale, and task scope. To address these limitations, we propose Multi-modal Situated Question Answering (MSQA), a large-scale multi-modal situated reasoning dataset, scalably collected leveraging 3D scene graphs and vision-language models (VLMs) across a diverse range of real-world 3D scenes. |
XIONGKUN LINGHU et. al. | arxiv-cs.CV | 2024-09-03 |
| 633 | How Privacy-Savvy Are Large Language Models? A Case Study on Compliance and Privacy Technical Review Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper seeks to address this gap by providing a comprehensive case study evaluating LLMs’ performance in privacy-related tasks such as privacy information extraction (PIE), legal and regulatory key point detection (KPD), and question answering (QA) with respect to privacy policies and data protection regulations. We introduce a Privacy Technical Review (PTR) framework, highlighting its role in mitigating privacy risks during the software development life-cycle. |
YANG LIU et. al. | arxiv-cs.CL | 2024-09-03 |
| 634 | Kvasir-VQA: A Text-Image Pair GI Tract Dataset Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce Kvasir-VQA, an extended dataset derived from the HyperKvasir and Kvasir-Instrument datasets, augmented with question-and-answer annotations to facilitate advanced machine learning tasks in Gastrointestinal (GI) diagnostics. |
SUSHANT GAUTAM et. al. | arxiv-cs.CV | 2024-09-02 |
| 635 | Language Models Benefit from Preparation with Elicited Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a simple prompting technique, called PREP, that involves using two instances of LMs: the first (LM1) generates relevant information, and the second (LM2) receives the information from the user and answers the question. |
Jiacan Yu; Hannah An; Lenhart K. Schubert; | arxiv-cs.CL | 2024-09-02 |
| 636 | Candidate-Heuristic In-Context Learning: A New Framework for Enhancing Medical Visual Question Answering with LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View |
XIAO LIANG et. al. | Inf. Process. Manag. | 2024-09-01 |
| 637 | Retrieval-Augmented Natural Language Reasoning for Explainable Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce a new VQA-NLE model, ReRe (Retrieval-augmented natural language Reasoning), using leverage retrieval information from the memory to aid in generating accurate answers and persuasive explanations without relying on complex networks and extra datasets. |
Su Hyeon Lim; Minkuk Kim; Hyeon Bae Kim; Seong Tae Kim; | arxiv-cs.CV | 2024-08-30 |
| 638 | MAPWise: Evaluating Vision-Language Models for Advanced Map Queries Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study investigates the efficacy of VLMs in answering questions based on choropleth maps, which are widely used for data analysis and representation. To facilitate and encourage research in this area, we introduce a novel map-based question-answering benchmark, consisting of maps from three geographical regions (United States, India, China), each containing 1000 questions. |
Srija Mukhopadhyay; Abhishek Rajgaria; Prerana Khatiwada; Vivek Gupta; Dan Roth; | arxiv-cs.CV | 2024-08-30 |
| 639 | LLM-Based Multi-Hop Question Answering with Knowledge Graph Integration in Evolving Environments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing methods for such knowledge editing still face difficulties with multi-hop questions that require accurate fact identification and sequential logical reasoning, particularly among numerous fact updates. To tackle these challenges, this paper introduces Graph Memory-based Editing for Large Language Models (GMeLLo), a straightforward and effective method that merges the explicit knowledge representation of Knowledge Graphs (KGs) with the linguistic flexibility of LLMs. |
RUIRUI CHEN et. al. | arxiv-cs.CL | 2024-08-28 |
| 640 | Can Visual Language Models Replace OCR-Based Visual Question Answering Pipelines in Production? A Case Study in Retail Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our study includes two commercial models, GPT-4V [16] and GPT-4o [17], as well as four open-source models: InternVL [5], LLaVA 1.5 [12], LLaVA-NeXT [13], and CogAgent [9]. |
Bianca Lamm; Janis Keuper; | arxiv-cs.CV | 2024-08-28 |
| 641 | Evidence-Enhanced Triplet Generation Framework for Hallucination Alleviation in Generative Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the hallucination in generative question answering (GQA) where the answer can not be derived from the document, we propose a novel evidence-enhanced triplet generation framework, EATQA, encouraging the model to predict all the combinations of (Question, Evidence, Answer) triplet by flipping the source pair and the target label to understand their logical relationships, i.e., predict Answer(A), Question(Q), and Evidence(E) given a QE, EA, and QA pairs, respectively. |
Haowei Du; Huishuai Zhang; Dongyan Zhao; | arxiv-cs.CL | 2024-08-27 |
| 642 | Question Answering System of Bridge Design Specification Based on Large Language Model Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Through the self-built question and answer task dataset, based on the tensorflow and keras deep learning platform framework, the model is constructed and trained to predict the start position and end position of the answer in the bridge design specification given by the user. |
Leye Zhang; Xiangxiang Tian; Hongjun Zhang; | arxiv-cs.CL | 2024-08-25 |
| 643 | IQA-EVAL: Automatic Evaluation of Human-Model Interactive Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce an automatic evaluation framework IQA-EVAL to achieve Interactive Question Answering Evaluations, more specifically, we introduce a LLM-based Evaluation Agent (LEA) that can: (1) simulate human behaviors to generate interactions with IQA models; (2) automatically evaluate the generated interactions. |
Ruosen Li; Ruochen Li; Barry Wang; Xinya Du; | arxiv-cs.CL | 2024-08-24 |
| 644 | Vintern-1B: An Efficient Multimodal Large Language Model for Vietnamese Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this report, we introduce Vintern-1B, a reliable 1-billion-parameters multimodal large language model (MLLM) for Vietnamese language tasks. |
KHANG T. DOAN et. al. | arxiv-cs.LG | 2024-08-22 |
| 645 | Assessing Modality Bias in Video Question Answering Benchmarks with Multimodal Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing video question-answering (VidQA) benchmarks and datasets often exhibit a bias toward a single modality, despite the goal of requiring advanced reasoning skills that integrate diverse modalities to answer the queries. In this work, we introduce the modality importance score (MIS) to identify such bias. |
JEAN PARK et. al. | arxiv-cs.LG | 2024-08-22 |
| 646 | Differentiating Choices Via Commonality for Multiple-Choice Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel MCQA model by differentiating choices through identifying and eliminating their commonality, called DCQA. |
WENQING DENG et. al. | arxiv-cs.CL | 2024-08-21 |
| 647 | Multimodal Datasets and Benchmarks for Reasoning About Dynamic Spatio-Temporality in Everyday Environments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We used a 3D simulator to create artificial video data with standardized annotations, aiming to aid in the development of Embodied AI. |
Takanori Ugai; Kensho Hara; Shusaku Egami; Ken Fukuda; | arxiv-cs.AI | 2024-08-21 |
| 648 | RConE: Rough Cone Embedding for Multi-Hop Logical Query Answering on Multi-Modal Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose RConE, an embedding method to capture the multi-modal information needed to answer a query. |
Mayank Kharbanda; Rajiv Ratn Shah; Raghava Mutharaju; | arxiv-cs.AI | 2024-08-21 |
| 649 | What Are The Limits of Cross-lingual Dense Passage Retrieval for Low-resource Languages? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we analyze the capabilities of the multi-lingual Dense Passage Retriever (mDPR) for extremely low-resource languages. |
Jie Wu; Zhaochun Ren; Suzan Verberne; | arxiv-cs.IR | 2024-08-21 |
| 650 | Mathematical Information Retrieval: Search and Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The framework is used to organize and relate the other core topics of the book, including interactions between people and systems, representing math formulas in sources, and evaluation. |
Richard Zanibbi; Behrooz Mansouri; Anurag Agarwal; | arxiv-cs.IR | 2024-08-21 |
| 651 | DocTabQA: Answering Questions from Long Documents Using Tables Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce the QTabA dataset, encompassing 300 financial documents, accompanied by manually annotated 1.5k question-table pairs. |
Haochen Wang; Kai Hu; Haoyu Dong; Liangcai Gao; | arxiv-cs.CL | 2024-08-21 |
| 652 | FoRAG: Factuality-optimized Retrieval Augmented Generation for Web-enhanced Long-form Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the emergence of various open source methods and web-enhanced commercial systems such as Bing Chat, two critical problems remain unsolved, i.e., the lack of factuality and clear logic in the generated long-form answers. In this paper, we remedy these issues via a systematic study on answer generation in web-enhanced LFQA. |
TIANCHI CAI et. al. | kdd | 2024-08-21 |
| 653 | DyGKT: Dynamic Graph Learning for Knowledge Tracing Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The three dynamical characteristics above contain the great potential to revolutionize the existing knowledge tracing methods. Along this line, we propose a Dynamic Graph-based Knowledge Tracing model, namely DyGKT. |
KE CHENG et. al. | kdd | 2024-08-21 |
| 654 | SOTOPIA-p: Interactive Learning of Socially Intelligent Language Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This social learning process is largely understudied by existing research on building language agents. Motivated by this gap, we propose an interactive learning method, SOTOPIA-p, that improves the social intelligence of language agents. |
RUIYI WANG et. al. | acl | 2024-08-20 |
| 655 | EWEK-QA : Enhanced Web and Efficient Knowledge Graph Retrieval for Citation-based Question Answering Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Second, web-retrieved contents are usually obtained by some simple heuristics such as fixed length or breakpoints which might lead to splitting information into pieces. To mitigate these issues, we propose our enhanced web and efficient knowledge graph (KG) retrieval solution (EWEK-QA) to enrich the content of the extracted knowledge fed to the system. |
MOHAMMAD DEHGHAN et. al. | acl | 2024-08-20 |
| 656 | FinTextQA: A Dataset for Long-form Financial Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work introduces FinTextQA, a novel dataset for long-form question answering (LFQA) in finance. |
JIAN CHEN et. al. | acl | 2024-08-20 |
| 657 | BizBench: A Quantitative Reasoning Benchmark for Business and Finance Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce BizBench, a benchmark for evaluating models� ability to reason about realistic financial problems. |
MICHAEL KRUMDICK et. al. | acl | 2024-08-20 |
| 658 | Learning Relational Decomposition of Queries for Question Answering from Tables Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: By learning to imitate a restricted subset of SQL-like algebraic operations, we demonstrate that their execution flow provides intermediate supervision steps that allow for increased generalization and structural reasoning compared to classical approaches. |
Rapha�l Mouravieff; Benjamin Piwowarski; Sylvain Lamprier; | acl | 2024-08-20 |
| 659 | Temporal Knowledge Question Answering Via Abstract Reasoning Induction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we address the challenge of enhancing temporal knowledge reasoning in Large Language Models (LLMs). |
Ziyang Chen; Dongfang Li; Xiang Zhao; Baotian Hu; Min Zhang; | acl | 2024-08-20 |
| 660 | TaPERA: Enhancing Faithfulness and Interpretability in Long-Form Table QA By Content Planning and Execution-based Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While Large language models based systems have made significant progress, it often hallucinates, especially when the task involves complex reasoning over tables. To tackle this issue, we propose a new LLM-based framework, TaPERA, for LFTQA tasks. |
Yilun Zhao; Lyuhao Chen; Arman Cohan; Chen Zhao; | acl | 2024-08-20 |
| 661 | MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose to select the most informative data for fine-tuning, thereby improving the efficiency of the fine-tuning process with comparative or even better accuracy on the open-domain QA task. |
XIUSI CHEN et. al. | acl | 2024-08-20 |
| 662 | Generate-then-Ground in Retrieval-Augmented Generation for Multi-hop Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the performance of this retrieve-then-read paradigm is constrained by the retriever and the inevitable noise in the retrieved documents. To mitigate these challenges, we introduce a novel generate-then-ground (GenGround) framework, synergizing the parametric knowledge of LLMs and external documents to solve a multi-hop question. |
ZHENGLIANG SHI et. al. | acl | 2024-08-20 |
| 663 | MARS: Meaning-Aware Response Scoring for Uncertainty Estimation in Generative LLMs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose Meaning-Aware Response Scoring (MARS) as an alternative to length-normalized scoring for UE methods. |
YAVUZ FARUK BAKMAN et. al. | acl | 2024-08-20 |
| 664 | Consistency Training By Synthetic Question Generation for Conversational Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: By citing a common modeling error prevalent in previous research, we introduce a new baseline and compare our model�s performance against it, demonstrating an improvement in results, particularly in later turns of the conversation, when dealing with questions that include a large historical context. |
Hamed Hemati; Hamid Beigy; | acl | 2024-08-20 |
| 665 | Exploring Hybrid Question Answering Via Program-based Prompting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose HProPro, a novel program-based prompting framework for the hybrid question answering task. |
QI SHI et. al. | acl | 2024-08-20 |
| 666 | PokeMQA: Programmable Knowledge Editing for Multi-hop Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We thus propose a framework, Programmable knowledge editing for Multi-hop Question Answering (PokeMQA), to decouple the jobs. |
HENGRUI GU et. al. | acl | 2024-08-20 |
| 667 | Putting People in LLMs’ Shoes: Generating Better Answers Via Question Rewriter Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, their effectiveness in QA is often undermined by the vagueness of user questions. To address this issue, we introduce single-round instance-level prompt optimization, referred to as question rewriter. |
Junhao Chen; Bowen Wang; Zhouqiang Jiang; Yuta Nakashima; | arxiv-cs.CL | 2024-08-20 |
| 668 | Few-shot Transfer Learning for Knowledge Base Question Answering: Fusing Supervised Models with In-Context Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce the problem of few-shot transfer learning for KBQA, where the target domain offers only a few labeled examples, but a large labeled training dataset is available in a source domain. |
MAYUR PATIDAR et. al. | acl | 2024-08-20 |
| 669 | Tree-of-Traversals: A Zero-Shot Reasoning Algorithm for Augmenting Black-box Language Models with Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce Tree-of-Traversals, a novel zero-shot reasoning algorithm that enables augmentation of black-box LLMs with one or more KGs. |
ELAN MARKOWITZ et. al. | acl | 2024-08-20 |
| 670 | ColBERT Retrieval and Ensemble Response Scoring for Language Model Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The Specializing Large Language Models for Telecom Networks challenge aimed to enhance the performance of two small language models, Phi-2 and Falcon-7B in telecommunication question answering. In this paper, we present our question answering systems for this challenge. |
Alex Gichamba; Tewodros Kederalah Idris; Brian Ebiyau; Eric Nyberg; Teruko Mitamura; | arxiv-cs.CL | 2024-08-20 |
| 671 | Multilingual Non-Factoid Question Answering with Answer Paragraph Selection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the scope of such datasets for low-resource languages remains limited, with only a few works centered on factoid-based QuADs and none on non-factoid QuADs. Therefore, this work presents MuNfQuAD, a multilingual QuAD with non-factoid questions. |
Ritwik Mishra; Sreeram Vennam; Rajiv Ratn Shah; Ponnurangam Kumaraguru; | arxiv-cs.CL | 2024-08-20 |
| 672 | To Generate or to Retrieve? On The Effectiveness of Artificial Contexts for Medical Open-Domain Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents MedGENIE, the first generate-then-read framework for multiple-choice question answering in medicine. |
Giacomo Frisoni; Alessio Cocchieri; Alex Presepi; Gianluca Moro; Zaiqiao Meng; | acl | 2024-08-20 |
| 673 | Answer Is All You Need: Instruction-following Text Embedding Via Answering The Question Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This work aims to build a text embedder that can capture characteristics of texts specified by user instructions clarifying the similarity criterion. |
LETIAN PENG et. al. | acl | 2024-08-20 |
| 674 | Domain Adaptation for Subjective Induction Questions Answering on Products By Adversarial Disentangled Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: It is hard for traditional methods to work well without considering the shift of domain patterns. To address this problem, we propose a novel domain-adaptive model. |
YUFENG ZHANG et. al. | acl | 2024-08-20 |
| 675 | SymKGQA: Few-Shot Knowledge Graph Question Answering Via Symbolic Program Generation and Execution Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recently, a new LF called KoPL has been introduced that explicitly models complex reasoning process step-by-step in a symbolic manner and has shown SOTA on KQA Pro in fully-supervised setting. Inspired by this, we propose SymKGQA framework that generates step-by-step Symbolic LF i. e. , KoPL in a few-shot in-context learning setting using LLM. |
Prerna Agarwal; Nishant Kumar; Srikanta Bedathur; | acl | 2024-08-20 |
| 676 | Hierarchical Retrieval-Augmented Generation Model with Rethink for Multi-hop Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Multi-hop Question Answering (QA) necessitates complex reasoning by integrating multiple pieces of information to resolve intricate questions. However, existing QA systems … |
XIAOMING ZHANG et. al. | ArXiv | 2024-08-20 |
| 677 | Paraphrasing in Affirmative Terms Improves Negation Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we experiment with seamless strategies that incorporate affirmative interpretations (i. e. , paraphrases without negation) to make models more robust against negation. |
MohammadHossein Rezaei; Eduardo Blanco; | acl | 2024-08-20 |
| 678 | MMToM-QA: Multimodal Theory of Mind Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: People can flexibly reason about another person�s mind based on conceptual representations (e. g. , goals, beliefs, plans) extracted from any available data. To address this, we introduce a multimodal Theory of Mind question answering (MMToM-QA) benchmark. |
CHUANYANG JIN et. al. | acl | 2024-08-20 |
| 679 | AutoAct: Automatic Agent Learning from Scratch for QA Via Self-Planning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we introduce AutoAct, an automatic agent learning framework for QA that does not rely on large-scale annotated data and synthetic planning trajectories from closed-source models (e. g. , GPT-4). |
SHUOFEI QIAO et. al. | acl | 2024-08-20 |
| 680 | Beyond Memorization: The Challenge of Random Memory Access in Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the mechanisms underlying knowledge storage and memory access within their parameters remain elusive. In this paper, we investigate whether a generative LM (e. g. , GPT-2) is able to access its memory sequentially or randomly. |
TONGYAO ZHU et. al. | acl | 2024-08-20 |
| 681 | Multimodal Fusion: Advancing Medical Visual Question-answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
Anjali Mudgal; Udbhav Kush; Aditya Kumar; Amir Jafari; | Neural Comput. Appl. | 2024-08-20 |
| 682 | Narrowing The Knowledge Evaluation Gap: Open-Domain Question Answering with Multi-Granularity Answers IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose GRANOLA QA, a novel evaluation setting where a predicted answer is evaluated in terms of accuracy and informativeness against a set of multi-granularity answers. |
Gal Yona; Roee Aharoni; Mor Geva; | acl | 2024-08-20 |
| 683 | FanOutQA: A Multi-Hop, Multi-Document Question Answering Benchmark for Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To evaluate complex reasoning in LLMs more fully, we present FanOutQA, a high-quality dataset of fan-out question-answer pairs and human-annotated decompositions with English Wikipedia as the knowledge base. |
Andrew Zhu; Alyssa Hwang; Liam Dugan; Chris Callison-Burch; | acl | 2024-08-20 |
| 684 | Towards Faithful and Robust LLM Specialists for Evidence-Based Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we systematically investigate how to robustly fine-tune LLMs for better source quality and answer attributability. |
Tobias Schimanski; Jingwei Ni; Mathias Kraus; Elliott Ash; Markus Leippold; | acl | 2024-08-20 |
| 685 | Never Lost in The Middle: Mastering Long-Context Question Answering with Position-Agnostic Decompositional Training IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The �lost in the middle� problem challenges most LLMs, referring to the dramatic decline in accuracy when correct information is located in the middle. To overcome this crucial issue, this paper proposes to enhance the information searching and reflection ability of LLMs in long contexts via specially designed tasks called Position-Agnostic Multi-step QA (PAM QA). |
JUNQING HE et. al. | acl | 2024-08-20 |
| 686 | SyllabusQA: A Course Logistics Question Answering Dataset Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce SyllabusQA, an open-source dataset with 63 real course syllabi covering 36 majors, containing 5,078 open-ended course logistics-related question-answer pairs that are diverse in both question types and answer formats. |
Nigel Fernandez; Alexander Scarlatos; Andrew Lan; | acl | 2024-08-20 |
| 687 | Safety Alignment in NLP Tasks: Weakly Aligned Summarization As An In-Context Attack Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our study, focusing on safety-sensitive documents obtained through adversarial attacks, reveals significant disparities in the safety alignment of various NLP tasks. |
Yu Fu; Yufei Li; Wen Xiao; Cong Liu; Yue Dong; | acl | 2024-08-20 |
| 688 | BeamAggR: Beam Aggregation Reasoning Over Multi-source Knowledge for Multi-hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, significant challenges still persist, including inaccurate and insufficient retrieval for complex questions, as well as difficulty in integrating multi-source knowledge. To address this, we propose Beam Aggregation Reasoning (BeamAggR), a reasoning framework for knowledge-intensive multi-hop QA. |
ZHENG CHU et. al. | acl | 2024-08-20 |
| 689 | Enhancing Biomedical Question Answering with Large Language Models Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In the field of Information Retrieval, biomedical question answering is a specialized task that focuses on answering questions related to medical and healthcare domains. The goal … |
Hua Yang; Shilong Li; Teresa Gonçalves; | Inf. | 2024-08-19 |
| 690 | MuRAR: A Simple and Effective Multimodal Retrieval and Answer Refinement Framework for Multimodal Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a simple and effective framework named MuRAR (Multimodal Retrieval and Answer Refinement). |
ZHENGYUAN ZHU et. al. | arxiv-cs.IR | 2024-08-16 |
| 691 | RealMedQA: A Pilot Biomedical Question Answering Dataset Containing Realistic Clinical Questions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we present RealMedQA, a dataset of realistic clinical questions generated by humans and an LLM. |
GREGORY KELL et. al. | arxiv-cs.CL | 2024-08-16 |
| 692 | W-RAG: Weakly Supervised Dense Retrieval in RAG for Open-domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Although dense retrieval demonstrates state-of-the-art performance, its training poses challenges due to the scarcity of ground-truth evidence, largely attributed to the high costs of human annotation. In this paper, we propose W-RAG, a method that draws weak training signals from the downstream task (such as OpenQA) of an LLM, and fine-tunes the retriever to prioritize passages that most benefit the task. |
Jinming Nian; Zhiyuan Peng; Qifan Wang; Yi Fang; | arxiv-cs.CL | 2024-08-15 |
| 693 | Assessing and Enhancing Large Language Models in Rare Disease Question-answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce a rare disease question-answering (ReDis-QA) dataset to evaluate the performance of LLMs in diagnosing rare diseases. |
GUANCHU WANG et. al. | arxiv-cs.CE | 2024-08-15 |
| 694 | LLaVA-Surg: Towards Multimodal Surgical Assistant Via Structured Surgical Video Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: One major contributing factor is the absence of datasets in the surgical field. In this paper, we create a new dataset, Surg-QA, consisting of 102,000 surgical video-instruction pairs, the largest of its kind so far. |
JIAJIE LI et. al. | arxiv-cs.CV | 2024-08-15 |
| 695 | IIU: Independent Inference Units for Knowledge-based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose Independent Inference Units (IIU) for fine-grained multi-modal reasoning to decompose intra-modal information by the functionally independent units. |
Yili Li; Jing Yu; Keke Gai; Gang Xiong; | arxiv-cs.CV | 2024-08-15 |
| 696 | QirK: Question Answering Via Intermediate Representation on Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We demonstrate QirK, a system for answering natural language questions on Knowledge Graphs (KG). |
JAN LUCA SCHEERER et. al. | arxiv-cs.DB | 2024-08-14 |
| 697 | Evaluating Fine-Tuning Efficiency of Human-Inspired Learning Strategies in Medical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This study evaluates the fine-tuning efficiency of five human-inspired strategies across four language models, three datasets, and both human- and LLM-labelled data in the context of medical question answering. |
Yushi Yang; Andrew M. Bean; Robert McCraith; Adam Mahdi; | arxiv-cs.CL | 2024-08-14 |
| 698 | Enhancing Visual Question Answering Through Ranking-Based Hybrid Training and Multimodal Fusion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Current VQA models struggle with complex questions due to limitations in capturing and integrating multimodal information effectively. To address these challenges, we propose the Rank VQA model, which leverages a ranking-inspired hybrid training strategy to enhance VQA performance. |
Peiyuan Chen; Zecheng Zhang; Yiping Dong; Li Zhou; Han Wang; | arxiv-cs.CV | 2024-08-14 |
| 699 | A RAG-Based Question-Answering Solution for Cyber-Attack Investigation and Attribution Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In the constantly evolving field of cybersecurity, it is imperative for analysts to stay abreast of the latest attack trends and pertinent information that aids in the investigation and attribution of cyber-attacks. In this work, we introduce the first question-answering (QA) model and its application that provides information to the cybersecurity experts about cyber-attacks investigations and attribution. |
Sampath Rajapaksha; Ruby Rani; Erisa Karafili; | arxiv-cs.CR | 2024-08-12 |
| 700 | Chain of Condition: Construct, Verify and Solve Conditions for Conditional Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing approaches struggle with CQA due to two challenges: (1) precisely identifying necessary conditions and the logical relationship, and (2) verifying conditions to detect any that are missing. In this paper, we propose a novel prompting approach, Chain of condition, by first identifying all conditions and constructing their logical relationships explicitly according to the document, then verifying whether these conditions are satisfied, finally solving the logical expression to indicate any missing conditions and generating the answer accordingly. |
Jiuheng Lin; Yuxuan Lai; Yansong Feng; | arxiv-cs.CL | 2024-08-10 |
| 701 | Surgical-VQLA++: Adversarial Contrastive Learning for Calibrated Robust Visual Question-Localized Answering in Robotic Surgery Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the inability of VQA models to visually indicate the regions of interest corresponding to the given questions results in incomplete comprehension of the surgical scene. To tackle this, we propose the surgical visual question localized-answering (VQLA) for precise and context-aware responses to specific queries regarding surgical images. |
LONG BAI et. al. | arxiv-cs.CV | 2024-08-09 |
| 702 | Towards A Generative Approach for Emotion Detection and Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: But can they perform emotional reasoning by concatenating `Let’s think step-by-step’ to the input prompt? In this paper we investigate this question along with introducing a novel approach to zero-shot emotion detection and emotional reasoning using LLMs. |
Ankita Bhaumik; Tomek Strzalkowski; | arxiv-cs.CL | 2024-08-09 |
| 703 | VideoQA in The Era of LLMs: An Empirical Study Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This work conducts a timely and comprehensive study of Video-LLMs’ behavior in VideoQA, aiming to elucidate their success and failure modes, and provide insights towards more human-like video understanding and question answering. |
JUNBIN XIAO et. al. | arxiv-cs.CV | 2024-08-08 |
| 704 | Enhancing Robustness of Retrieval-Augmented Language Models with In-Context Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, RALMs still struggle with unanswerable queries, where the retrieved contexts do not contain the correct answer, and with conflicting information, where different sources provide contradictory answers due to imperfect retrieval. This study introduces an in-context learning-based approach to enhance the reasoning capabilities of RALMs, making them more robust in imperfect retrieval scenarios. |
Seong-Il Park; Seung-Woo Choi; Na-Hyun Kim; Jay-Yoon Lee; | arxiv-cs.CL | 2024-08-08 |
| 705 | Enhancing Healthcare Through Large Language Models: A Study on Medical Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a detailed study of various LLMs trained on the MedQuAD medical question-answering dataset, with a focus on identifying the most effective model for providing accurate medical information. |
Haoran Yu; Chang Yu; Zihan Wang; Dongxian Zou; Hao Qin; | arxiv-cs.CL | 2024-08-07 |
| 706 | Targeted Visual Prompting for Medical Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address this, region-based questions have been proposed as a means to assess and enhance actual visual understanding through compositional evaluation. To combine these two perspectives, this paper introduces targeted visual prompting to equip MLLMs with region-based questioning capabilities. |
Sergio Tascon-Morales; Pablo Márquez-Neila; Raphael Sznitman; | arxiv-cs.CV | 2024-08-06 |
| 707 | Leveraging Inter-Chunk Interactions for Enhanced Retrieval in Large Language Model-Based Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Previous research typically handles paragraphs from external documents in isolation, resulting in a lack of context and ambiguous references, particularly in multi-document and complex tasks. To overcome these challenges, we propose a new retrieval framework IIER, that leverages Inter-chunk Interactions to Enhance Retrieval. |
TIEZHENG GUO et. al. | arxiv-cs.CL | 2024-08-05 |
| 708 | Developing PUGG for Polish: A Modern Approach to KBQA, MRC, and IR Dataset Construction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We executed this pipeline and introduced the PUGG dataset, the first Polish KBQA dataset, and novel datasets for MRC and IR. |
ALBERT SAWCZYN et. al. | arxiv-cs.AI | 2024-08-05 |
| 709 | Entity Retrieval for Answering Entity-Centric Questions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we propose Entity Retrieval, a novel retrieval method which rather than relying on question-document similarity, depends on the salient entities within the question to identify the retrieval documents. |
Hassan S. Shavarani; Anoop Sarkar; | arxiv-cs.IR | 2024-08-05 |
| 710 | Enhancing Relation Classification Based on GCN Multi-hop Knowledge Graph Question Answering Model of Knowledge Reasoning Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Existing multi-hop knowledge graph question answering approaches primarily infer answers by predicting sequential relation paths or aggregating latent graph features, achieving … |
YING WANG et. al. | 2024 International Conference on Asian Language Processing … | 2024-08-04 |
| 711 | MMVQA: A Comprehensive Dataset for Investigating Multipage Multimodal Information Retrieval in PDF-based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The paper introduces PDF-MVQA, tailored for research journal articles, encompassing multiple pages and multimodal retrieval. |
Yihao Ding; Kaixuan Ren; Jiabin Huang; Siwen Luo; Soyeon Caren Han; | ijcai | 2024-08-03 |
| 712 | ScreenAI: A Vision-Language Model for UI and Infographics Understanding IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce ScreenAI, a vision-language model that specializes in UI and infographics understanding. |
GILLES BAECHLER et. al. | ijcai | 2024-08-03 |
| 713 | KG-CoT: Chain-of-Thought Prompting of Large Language Models Over Knowledge Graphs for Knowledge-Aware Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, fragmented knowledge facts extracted by knowledge retrievers fail to provide explicit and coherent reasoning paths for improving LLM reasoning. To address these challenges, we propose KG-CoT, a novel knowledge-augmented paradigm that leverages a small-scale step-by-step graph reasoning model to reason over knowledge graphs (KGs) and utilizes a reasoning path generation method to generate chains of reasoning with high confidence for large-scale LLMs. |
Ruilin Zhao; Feng Zhao; Long Wang; Xianzhi Wang; Guandong Xu; | ijcai | 2024-08-03 |
| 714 | KnowledgeHub: An End-to-End Tool for Assisted Scientific Discovery Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper describes the KnowledgeHub tool, a scientific literature Information Extraction (IE) and Question Answering (QA) pipeline. |
SHINNOSUKE TANAKA et. al. | ijcai | 2024-08-03 |
| 715 | GigaPevt: Multimodal Medical Assistant Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This demo paper presents GigaPevt, the first multimodal medical assistant that combines the dialog capabilities of large language models with specialized medical models. |
PAVEL BLINOV et. al. | ijcai | 2024-08-03 |
| 716 | Graph Collaborative Expert Finding with Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we try to address the limitation of current models that typically neglect the intrinsic high-order connectivity within expert-question interactions, which is pivotal for collaborative effects. |
Qiyao Peng; Wenjun Wang; Hongtao Liu; Cuiying Huo; Minglai Shao; | ijcai | 2024-08-03 |
| 717 | Adaptive Contrastive Decoding in Retrieval-Augmented Generation for Handling Noisy Contexts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: When using large language models (LLMs) in knowledge-intensive tasks, such as open-domain question answering, external context can bridge the gap between external knowledge and the LLMs’ parametric knowledge. |
YOUNA KIM et. al. | arxiv-cs.CL | 2024-08-02 |
| 718 | DebateQA: Evaluating Question Answering on Debatable Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, traditional QA benchmarks assume fixed answers are inadequate for this purpose. To address this, we introduce DebateQA, a dataset of 2,941 debatable questions, each accompanied by multiple human-annotated partial answers that capture a variety of perspectives. |
Rongwu Xu; Xuan Qi; Zehan Qi; Wei Xu; Zhijiang Guo; | arxiv-cs.CL | 2024-08-02 |
| 719 | BioRAG: A RAG-LLM Framework for Biological Question Reasoning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The question-answering system for Life science research, which is characterized by the rapid pace of discovery, evolving insights, and complex interactions among knowledge entities, presents unique challenges in maintaining a comprehensive knowledge warehouse and accurate information retrieval. To address these issues, we introduce BioRAG, a novel Retrieval-Augmented Generation (RAG) with the Large Language Models (LLMs) framework. |
CHENGRUI WANG et. al. | arxiv-cs.CL | 2024-08-02 |
| 720 | MKEAH： Multimodal Knowledge Extraction and Accumulation Based on Hyperplane Embedding for Knowledge-based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
HENG ZHANG et. al. | Virtual Real. Intell. Hardw. | 2024-08-01 |
| 721 | Automated Question Answering for Unveiling Leadership Dynamics in U.S. Presidential Speeches Related Papers Related Patents Related Grants Related Venues Related Experts View |
Krzysztof Rybinski; | Expert Syst. Appl. | 2024-08-01 |
| 722 | Transformer-based Vision-language Alignment for Robot Navigation and Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
Haonan Luo; Ziyu Guo; Zhenyu Wu; Fei Teng; Tian-Jie Li; | Inf. Fusion | 2024-08-01 |
| 723 | Decomposed Prompting to Answer Questions on A Course Discussion Board Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose and evaluate a question-answering system that uses decomposed prompting to classify and answer student questions on a course discussion board. |
BRANDON JAIPERSAUD et. al. | arxiv-cs.CL | 2024-07-30 |
| 724 | Boosting Audio Visual Question Answering Via Key Semantic-Aware Cues Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a Temporal-Spatial Perception Model (TSPM), which aims to empower the model to perceive key visual and auditory cues related to the questions. |
Guangyao Li; Henghui Du; Di Hu; | arxiv-cs.CV | 2024-07-30 |
| 725 | Advancing Vietnamese Visual Question Answering with Transformer and Convolutional Integration Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Despite the prevalence of approaches in English, there is a notable lack of systems specifically developed for certain languages, particularly Vietnamese. This study aims to bridge this gap by conducting comprehensive experiments on the Vietnamese Visual Question Answering (ViVQA) dataset, demonstrating the effectiveness of our proposed model. |
Ngoc Son Nguyen; Van Son Nguyen; Tung Le; | arxiv-cs.CV | 2024-07-30 |
| 726 | Advancing Multimodal Large Language Models in Chart Question Answering with Visualization-Referenced Instruction Tuning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To fill the gap, we propose a visualization-referenced instruction tuning approach to guide the training dataset enhancement and model development. |
Xingchen Zeng; Haichuan Lin; Yilin Ye; Wei Zeng; | arxiv-cs.CV | 2024-07-29 |
| 727 | AdaCoder: Adaptive Prompt Compression for Programmatic Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, they often require long input prompts to provide the LLM with sufficient API usage details to generate relevant code. To address this limitation, we propose AdaCoder, an adaptive prompt compression framework for VPMs. |
Mahiro Ukai; Shuhei Kurita; Atsushi Hashimoto; Yoshitaka Ushiku; Nakamasa Inoue; | arxiv-cs.AI | 2024-07-28 |
| 728 | Answerability Fields: Answerable Location Estimation Via Diffusion Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Answerability Fields, a novel approach to predicting answerability within complex indoor environments. |
Daichi Azuma; Taiki Miyanishi; Shuhei Kurita; Koya Sakamoto; Motoaki Kawanabe; | arxiv-cs.CV | 2024-07-26 |
| 729 | The Geometry of Queries: Query-Based Innovations in Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce Query-Based Retrieval Augmented Generation (QB-RAG), a novel approach that pre-computes a database of potential queries from a content base using LLMs. |
Eric Yang; Jonathan Amar; Jong Ha Lee; Bhawesh Kumar; Yugang Jia; | arxiv-cs.LG | 2024-07-25 |
| 730 | ScholarChemQA: Unveiling The Power of Language Models in Chemical Research Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Correspondingly, we introduce a QAMatch model, specifically designed to effectively answer chemical questions by fully leveraging our collected data. |
XIUYING CHEN et. al. | arxiv-cs.CL | 2024-07-23 |
| 731 | KaPQA: Knowledge-Augmented Product Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, accurately assessing the performance of these applications remains a challenge, mainly due to the lack of suitable benchmarks that effectively simulate real-world scenarios. To address this challenge, we introduce two product question-answering (QA) datasets focused on Adobe Acrobat and Photoshop products to help evaluate the performance of existing models on domain-specific product QA tasks. |
SWETHA EPPALAPALLY et. al. | arxiv-cs.CL | 2024-07-22 |
| 732 | Caption Matters: A New Perspective for Knowledge-based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
BIN FENG et. al. | Knowl. Inf. Syst. | 2024-07-22 |
| 733 | OMoS-QA: A Dataset for Cross-Lingual Extractive Question Answering in A German Migration Context Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we present OMoS-QA, a dataset of German and English questions paired with relevant trustworthy documents and manually annotated answers, specifically tailored to this scenario. |
Steffen Kleinle; Jakob Prange; Annemarie Friedrich; | arxiv-cs.CL | 2024-07-22 |
| 734 | RadioRAG: Factual Large Language Models for Enhanced Diagnostics in Radiology Using Online Retrieval Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For radiology, RadioRAG establishes a robust framework that substantially improves diagnostic accuracy and factuality in radiological question answering. |
SOROOSH TAYEBI ARASTEH et. al. | arxiv-cs.CL | 2024-07-22 |
| 735 | RadioRAG: Factual Large Language Models for Enhanced Diagnostics in Radiology Using Dynamic Retrieval Augmented Generation Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Large language models (LLMs) have advanced the field of artificial intelligence (AI) in medicine. However LLMs often generate outdated or inaccurate information based on static … |
SOROOSH TAYEBI et. al. | ArXiv | 2024-07-22 |
| 736 | End-to-End Video Question Answering with Frame Scoring Mechanisms and Adaptive Sampling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Simply uniformly sampling frames or indiscriminately aggregating frame-level visual features often falls short in capturing the nuanced and relevant contexts of videos to well perform VideoQA. To mitigate these issues, we propose VidF4, a novel VideoQA framework equipped with tailored frame selection strategy for effective and efficient VideoQA. |
JIANXIN LIANG et. al. | arxiv-cs.CV | 2024-07-21 |
| 737 | Customized Retrieval Augmented Generation and Benchmarking for EDA Tool Documentation QA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Off-the-shelf RAG flows are well pretrained on general-purpose documents, yet they encounter significant challenges when being applied to knowledge-intensive vertical domains, such as electronic design automation (EDA). This paper addresses such issue by proposing a customized RAG framework along with three domain-specific techniques for EDA tool documentation QA, including a contrastive learning scheme for text embedding model fine-tuning, a reranker distilled from proprietary LLM, and a generative LLM fine-tuned with high-quality domain corpus. |
Yuan Pu; Zhuolun He; Tairu Qiu; Haoyuan Wu; Bei Yu; | arxiv-cs.CL | 2024-07-21 |
| 738 | Knowledge Acquisition Disentanglement for Knowledge-based Visual Question Answering with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Furthermore, the “forward-only” answering process fails to explicitly capture the knowledge needs of LLMs, which can further hurt answering quality. To cope with the above limitations, we propose DKA: Disentangled Knowledge Acquisition from LLM feedback, a training-free framework that disentangles knowledge acquisition to avoid confusion and uses LLM’s feedback to specify the required knowledge. |
WENBIN AN et. al. | arxiv-cs.CV | 2024-07-21 |
| 739 | Generalization V.s. Memorization: Tracing Language Models’ Capabilities Back to Pretraining Data IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To effectively capture task-specific pretraining data frequency, we propose a novel task-gram language model, which is built by counting the co-occurrence of semantically related $n$-gram pairs from task inputs and outputs in the pretraining corpus. |
XINYI WANG et. al. | arxiv-cs.CL | 2024-07-20 |
| 740 | Evaluating Language Models As Risk Scores Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we focus on the use of LLMs as risk scores for unrealizable prediction tasks. |
André F. Cruz; Moritz Hardt; Celestine Mendler-Dünner; | arxiv-cs.LG | 2024-07-19 |
| 741 | Clinical Reading Comprehension with Encoder-Decoder Models Enhanced By Direct Preference Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we combine encoder-decoder models with the direct preference optimization (DPO) method to improve over prior state of the art for the RadQA radiology question answering task by 12-15 F1 points. |
Md Sultan Al Nahian; Ramakanth Kavuluru; | arxiv-cs.IR | 2024-07-18 |
| 742 | INDIC QA BENCHMARK: A Multilingual Benchmark to Evaluate Question Answering Capability of LLMs for Indic Languages Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Large Language Models (LLMs) perform well on unseen tasks in English, but their abilities in non English languages are less explored due to limited benchmarks and training data. To bridge this gap, we introduce the Indic QA Benchmark, a large dataset for context grounded question answering in 11 major Indian languages, covering both extractive and abstractive tasks. |
ABHISHEK KUMAR SINGH et. al. | arxiv-cs.LG | 2024-07-18 |
| 743 | Visual Haystacks: A Vision-Centric Needle-In-A-Haystack Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Towards a solution, we introduce MIRAGE (Multi-Image Retrieval Augmented Generation), an open-source, lightweight visual-RAG framework that processes up to 10k images on a single 40G A100 GPU — far surpassing the 1k-image limit of contemporary models. |
TSUNG-HAN WU et. al. | arxiv-cs.CV | 2024-07-18 |
| 744 | Retrieve, Summarize, Plan: Advancing Multi-hop Question Answering with An Iterative Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel iterative RAG method called ReSP, equipped with a dual-function summarizer. |
Zhouyu Jiang; Mengshu Sun; Lei Liang; Zhiqiang Zhang; | arxiv-cs.CL | 2024-07-17 |
| 745 | EchoSight: Advancing Visual-Language Models with Wiki Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce EchoSight, a novel multimodal Retrieval-Augmented Generation (RAG) framework that enables large language models (LLMs) to answer visual questions requiring fine-grained encyclopedic knowledge. |
Yibin Yan; Weidi Xie; | arxiv-cs.CV | 2024-07-17 |
| 746 | Continual Learning for Temporal-Sensitive Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we explore an emerging research area of Continual Learning for Temporal Sensitive Question Answering (CLTSQA). |
WANQI YANG et. al. | arxiv-cs.CL | 2024-07-17 |
| 747 | Evaluating Search Engines and Large Language Models for Answering Health Questions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This study compares the performance of four popular SEs, seven LLMs, and retrieval-augmented (RAG) variants in answering 150 health-related questions from the TREC Health Misinformation (HM) Track. |
Marcos Fernández-Pichel; Juan C. Pichel; David E. Losada; | arxiv-cs.IR | 2024-07-17 |
| 748 | TurkishMMLU: Measuring Massive Multitask Language Understanding in Turkish Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce the first multitask, multiple-choice Turkish QA benchmark, TurkishMMLU, to evaluate LLMs’ understanding of the Turkish language. |
Arda Yüksel; Abdullatif Köksal; Lütfi Kerem Şenel; Anna Korhonen; Hinrich Schütze; | arxiv-cs.CL | 2024-07-17 |
| 749 | Localizing and Mitigating Errors in Long-form Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This work introduces HaluQuestQA, the first hallucination dataset with localized error annotations for human-written and model-generated LFQA answers. |
Rachneet Sachdeva; Yixiao Song; Mohit Iyyer; Iryna Gurevych; | arxiv-cs.CL | 2024-07-16 |
| 750 | Reasoning with Large Language Models, A Survey IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We provide an in-depth coverage of core approaches and open problems, and we propose a research agenda for the near future. |
ASKE PLAAT et. al. | arxiv-cs.AI | 2024-07-16 |
| 751 | TM-PATHVQA:90000+ Textless Multilingual Questions for Medical Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, this work implements a speech-based VQA system by introducing a Textless Multilingual Pathological VQA (TMPathVQA) dataset, an expansion of the PathVQA dataset, containing spoken questions in English, German & French. |
Tonmoy Rajkhowa; Amartya Roy Chowdhury; Sankalp Nagaonkar; Achyut Mani Tripathi; | arxiv-cs.CV | 2024-07-16 |
| 752 | Multimodal Reranking for Knowledge-Intensive Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce an additional module, a multi-modal reranker, to improve the ranking quality of knowledge candidates for answer generation. |
Haoyang Wen; Honglei Zhuang; Hamed Zamani; Alexander Hauptmann; Michael Bendersky; | arxiv-cs.CL | 2024-07-16 |
| 753 | Unraveling The Truth: Do VLMs Really Understand Charts? A Deep Dive Into Consistency and Robustness Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We investigate two key aspects: 1) the models’ ability to handle varying levels of chart and question complexity, and 2) their robustness across different visual representations of the same underlying data. |
SRIJA MUKHOPADHYAY et. al. | arxiv-cs.CL | 2024-07-15 |
| 754 | Evaluation of RAG Metrics for Question Answering in The Telecom Domain Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Retrieval Augmented Generation (RAG) is widely used to enable Large Language Models (LLMs) perform Question Answering (QA) tasks in various domains. However, RAG based on … |
SUJOY ROYCHOWDHURY et. al. | ArXiv | 2024-07-15 |
| 755 | Graphusion: Leveraging Large Language Models for Scientific Knowledge Graph Fusion and Construction in NLP Education Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce Graphusion, a zero-shot KGC framework from free text. |
RUI YANG et. al. | arxiv-cs.CL | 2024-07-15 |
| 756 | Unraveling The Truth: Do LLMs Really Understand Charts? A Deep Dive Into Consistency and Robustness Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Chart question answering (CQA) is a crucial area of Visual Language Understanding. However, the robustness and consistency of current Visual Language Models (VLMs) in this field … |
SRIJA MUKHOPADHYAY et. al. | Conference on Empirical Methods in Natural Language … | 2024-07-15 |
| 757 | RAG-Ex: A Generic Framework for Explaining Retrieval Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce RAG-Ex, a model- and language-agnostic explanation framework that presents approximate explanations to the users revealing why the LLMs possibly generated a piece of text as a response, given the user input. |
Viju Sudhi; Sinchana Ramakanth Bhat; Max Rudat; Roman Teucher; | sigir | 2024-07-14 |
| 758 | A Question-Answering Assistant Over Personal Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on a fine-grained schema customized for PKG, the PKGQA system in this paper comprises Symbolic Semantic Parsing, Frequently Asked Question (FAQ) Semantic Matching, and Neural Semantic Parsing modules, which are designed to take into account both accuracy and efficiency. |
LINGYUAN LIU et. al. | sigir | 2024-07-14 |
| 759 | Towards Robust QA Evaluation Via Open LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite their remarkable capabilities, proprietary LLMs are costly and subject to internal changes that can affect their output, which inhibits the reproducibility of their results and limits the widespread adoption of LLM-based evaluation. In this demo, we aim to use publicly available LLMs for standardizing LLM-based QA evaluation. |
Ehsan Kamalloo; Shivani Upadhyay; Jimmy Lin; | sigir | 2024-07-14 |
| 760 | GenSco: Can Question Decomposition Based Passage Alignment Improve Question Answering? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate whether providing aligned context via a carefully selected passage sequence leads to better answer generation by the LLM for multi-hop QA. |
Barah Fazili; Koustava Goswami; Natwar Modani; Inderjeet Nair; | arxiv-cs.CL | 2024-07-14 |
| 761 | Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a novel customer service question-answering method that amalgamates RAG with a knowledge graph (KG). |
ZHENTAO XU et. al. | sigir | 2024-07-14 |
| 762 | ArabicaQA: A Comprehensive Dataset for Arabic Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we address the significant gap in Arabic natural language processing (NLP) resources by introducing ArabicaQA, the first large-scale dataset for machine reading comprehension and open-domain question answering in Arabic. |
ABDELRAHMAN ABDALLAH et. al. | sigir | 2024-07-14 |
| 763 | Are Large Language Models Good at Utility Judgments? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: (iv) We propose a k-sampling, listwise approach to reduce the dependency of LLMs on the sequence of input passages, thereby facilitating subsequent answer generation. |
HENGRAN ZHANG et. al. | sigir | 2024-07-14 |
| 764 | CIQA: A Coding Inspired Question Answering Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel domain-agnostic model to address the problem by leveraging domain-specific and open-source code libraries. |
Mousa Arraf; Kira Radinsky; | sigir | 2024-07-14 |
| 765 | ChroniclingAmericaQA: A Large-scale Question Answering Dataset Based on Historical American Newspaper Pages Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To further contribute to advancing QA and MRC tasks and to overcome the limitation of previous datasets, we introduce ChroniclingAmericaQA, a large-scale temporal QA dataset with 487K question-answer pairs created based on the historical newspaper collection Chronicling America. |
Bhawna Piryani; Jamshid Mozafari; Adam Jatowt; | sigir | 2024-07-14 |
| 766 | Let Me Show You Step By Step: An Interpretable Graph Routing Network for Knowledge-based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel interpretable graph routing network (GRN) which explicitly conducts entity routing over a constructed scene knowledge graph step by step for KB-VQA. |
DUOKANG WANG et. al. | sigir | 2024-07-14 |
| 767 | Boosting Conversational Question Answering with Fine-Grained Retrieval-Augmentation and Self-Check IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a conversation-level RAG (ConvRAG) approach, which incorporates fine-grained retrieval augmentation and self-check for conversational question answering (CQA). |
LINHAO YE et. al. | sigir | 2024-07-14 |
| 768 | Can LLMs Master Math? Investigating Large Language Models on Math Stack Exchange IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we follow a two-step approach to investigating the proficiency of LLMs in answering mathematical questions. |
ANKIT SATPUTE et. al. | sigir | 2024-07-14 |
| 769 | NativQA: Multilingual Culturally-Aligned Natural Query for LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we propose a scalable, language-independent framework, NativQA, to seamlessly construct culturally and regionally aligned QA datasets in native languages, for LLM evaluation and tuning. |
MD. ARID HASAN et. al. | arxiv-cs.CL | 2024-07-13 |
| 770 | One Stone, Four Birds: A Comprehensive Solution for QA System Using Supervised Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents a novel and comprehensive solution to enhance both the robustness and efficiency of question answering (QA) systems through supervised contrastive learning (SCL). |
Bo Wang; Tsunenori Mine; | arxiv-cs.CL | 2024-07-12 |
| 771 | SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce SPIQA (Scientific Paper Image Question Answering), the first large-scale QA dataset specifically designed to interpret complex figures and tables within the context of scientific research articles across various domains of computer science. |
Shraman Pramanick; Rama Chellappa; Subhashini Venugopalan; | arxiv-cs.CL | 2024-07-12 |
| 772 | Bridging The Gap Between Information Seeking and Product Search Systems: Q&A Recommendation for E-commerce Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The recent success of Large Language Models (LLMs) has opened up an opportunity to bridge the gap between the two tasks to help customers achieve their goals quickly and effectively by integrating conversational QA within product search. In this paper, we propose to recommend users Question-Answer (Q&A) pairs that are relevant to their product search and can help them make a purchase decision. |
Saar Kuzi; Shervin Malmasi; | arxiv-cs.CL | 2024-07-12 |
| 773 | Segmentation-guided Attention for Visual Question Answering from Remote Sensing Images Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose to embed an attention mechanism guided by segmentation into a RSVQA pipeline. |
LUCREZIA TOSATO et. al. | arxiv-cs.CV | 2024-07-11 |
| 774 | Uncertainty Estimation of Large Language Models in Medical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we benchmark popular UE methods with different model sizes on medical question-answering datasets. |
Jiaxin Wu; Yizhou Yu; Hong-Yu Zhou; | arxiv-cs.CL | 2024-07-11 |
| 775 | Examining Long-Context Large Language Models for Environmental Review Document Comprehension Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Long context and retrieval-augmented generation (RAG) are two such methods that have recently gained popularity. In this work, we examine the benefits of both of these techniques by utilizing question answering (QA) task in a niche domain. |
HUNG PHAN et. al. | arxiv-cs.CL | 2024-07-09 |
| 776 | Question Answering with Texts and Tables Through Deep Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a novel architecture to generate multi-hop answers to open domain questions that require information from texts and tables, using the Open Table-and-Text Question Answering dataset for validation and training. |
MARCOS M. JOSÉ et. al. | arxiv-cs.CL | 2024-07-05 |
| 777 | Sponsored Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present the first formal analysis of a sponsored QA platform. |
Tommy Mordo; Moshe Tennenholtz; Oren Kurland; | arxiv-cs.GT | 2024-07-05 |
| 778 | On Scalable Oversight with Weak LLMs Judging Strong LLMs IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we study debate, where two AI’s compete to convince a judge; consultancy, where a single AI tries to convince a judge that asks questions; and compare to a baseline of direct question-answering, where the judge just answers outright without the AI. |
ZACHARY KENTON et. al. | arxiv-cs.LG | 2024-07-05 |
| 779 | Second Place Solution of WSDM2023 Toloka Visual Question Answering Challenge Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present our solution for the WSDM2023 Toloka Visual Question Answering Challenge. |
Xiangyu Wu; Zhouyang Chi; Yang Yang; Jianfeng Lu; | arxiv-cs.CV | 2024-07-05 |
| 780 | Black-box Model Ensembling for Textual and Visual Question Answering Via Information Fusion Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, fine-tuning these models is either difficult, as it requires access via APIs, rendering them as black-boxes, or costly due to the need of tuning a large number of parameters. To address this, we introduce InfoSel, a data-efficient ensemble method that learns to dynamically pick the winner from existing black-box models for predictions on both textual and multimodal visual question answering tasks. |
Yuxi Xia; Kilm Zaporojets; Benjamin Roth; | arxiv-cs.CL | 2024-07-04 |
| 781 | Leveraging Topic Specificity and Social Relationships for Expert Finding in Community Question Answering Platforms Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present TUEF, a Topic-oriented User-Interaction model for Expert Finding, which aims to fully and transparently leverage the heterogeneous information available within online question-answering communities. |
Maddalena Amendola; Andrea Passarella; Raffaele Perego; | arxiv-cs.IR | 2024-07-04 |
| 782 | STOC-TOT: Stochastic Tree-of-Thought with Constrained Decoding for Complex Reasoning in Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose STOC-TOT, a stochastic tree-of-thought reasoning prompting method with constrained decoding for MHQA and conduct a detailed comparison with other reasoning prompts on different question types and reasoning types. |
Zhenyu Bi; Daniel Hajialigol; Zhongkai Sun; Jie Hao; Xuan Wang; | arxiv-cs.CL | 2024-07-04 |
| 783 | UnSeenTimeQA: Time-Sensitive Question-Answering Beyond LLMs’ Memorization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces UnSeenTimeQA, a novel data contamination-free time-sensitive question-answering (TSQA) benchmark. |
MD NAYEM UDDIN et. al. | arxiv-cs.CL | 2024-07-03 |
| 784 | FSM: A Finite State Machine Based Zero-Shot Prompting Paradigm for Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a prompting method, Finite State Machine (FSM) to enhance the reasoning capabilities of LLM for complex tasks in addition to improved effectiveness and trustworthiness. |
XIAOCHEN WANG et. al. | arxiv-cs.CL | 2024-07-03 |
| 785 | VDMA: Video Question Answering with Dynamically Generated Multi-Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Video Question Answering with Dynamically Generated Multi-Agents (VDMA). |
Noriyuki Kugo; Tatsuya Ishibashi; Kosuke Ono; Yuji Sato; | arxiv-cs.CV | 2024-07-03 |
| 786 | Align and Aggregate: Compositional Reasoning with Video Alignment and Answer Aggregation for Video Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the recent progress made in Video Question-Answering (VideoQA), these methods typically function as black-boxes, making it difficult to understand their reasoning processes and perform consistent compositional reasoning. To address these challenges, we propose a \textit{model-agnostic} Video Alignment and Answer Aggregation (VA$^{3}$) framework, which is capable of enhancing both compositional consistency and accuracy of existing VidQA methods by integrating video aligner and answer aggregator modules. |
Zhaohe Liao; Jiangtong Li; Li Niu; Liqing Zhang; | arxiv-cs.CV | 2024-07-03 |
| 787 | DSAMR: Dual-Stream Attention Multi-hop Reasoning for Knowledge-based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
YANHAN SUN et. al. | Expert Syst. Appl. | 2024-07-01 |
| 788 | M2QA: Multi-domain Multilingual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This prevents the transfer of NLP systems from well-resourced languages and domains to non-dominant language-domain combinations. To address this gap, we introduce M2QA, a multi-domain multilingual question answering benchmark. |
LEON ENGLÄNDER et. al. | arxiv-cs.CL | 2024-07-01 |
| 789 | Explainable Knowledge Reasoning Via Thought Chains for Knowledge-based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
Chen Qiu; Zhiqiang Xie; Maofu Liu; Huijun Hu; | Inf. Process. Manag. | 2024-07-01 |
| 790 | Event-centric Hierarchical Hyperbolic Graph for Multi-hop Question Answering Over Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View |
Xun Zhu; Wang Gao; Tianyu Li; Wenguang Yao; Hongtao Deng; | Eng. Appl. Artif. Intell. | 2024-07-01 |
| 791 | The Solution for The ICCV 2023 Perception Test Challenge 2023 — Task 6 — Grounded VideoQA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a grounded video question-answering solution. |
Hailiang Zhang; Dian Chao; Zhihao Guan; Yang Yang; | arxiv-cs.CV | 2024-07-01 |
| 792 | Incorporating Multi-perspective Information Into Reinforcement Learning to Address Multi-hop Knowledge Graph Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
CHUANYANG GONG et. al. | Expert Syst. Appl. | 2024-07-01 |
| 793 | Calibrated Large Language Models for Binary Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel approach that utilizes the inductive Venn–Abers predictor (IVAP) to calibrate the probabilities associated with the output tokens corresponding to the binary labels. |
Patrizio Giovannotti; Alexander Gammerman; | arxiv-cs.CL | 2024-07-01 |
| 794 | Learning A Mixture of Conditional Gating Blocks for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
Qiang Sun; Yan-Wei Fu; Xiang-Yang Xue; | J. Comput. Sci. Technol. | 2024-07-01 |
| 795 | Dynamic Few-Shot Learning for Knowledge Graph Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we introduce a novel approach called Dynamic Few-Shot Learning (DFSL). |
Jacopo D’Abramo; Andrea Zugarini; Paolo Torroni; | arxiv-cs.CL | 2024-07-01 |
| 796 | Hierarchical Memory for Long Video QA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper describes our champion solution to the LOVEU Challenge @ CVPR’24, Track 1 (Long Video VQA). |
YIQIN WANG et. al. | arxiv-cs.CV | 2024-06-30 |
| 797 | BioKGBench: A Knowledge Graph Checking Benchmark of AI Agent for Biomedical Science Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: On the widely used popular knowledge graph, we discover over 90 factual errors which provide scenarios for agents to make discoveries and demonstrate the effectiveness of our approach. |
XINNA LIN et. al. | arxiv-cs.CL | 2024-06-29 |
| 798 | HCCL: Hierarchical Counterfactual Contrastive Learning for Robust Visual Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Despite most state-of-the-art models having achieved amazing performance in visual question answering (VQA), they usually utilize biases to answer the question. Recently, some … |
Dongze Hao; Qunbo Wang; Xinxin Zhu; Jing Liu; | ACM Transactions on Multimedia Computing, Communications … | 2024-06-27 |
| 799 | FlowVQA: Mapping Multimodal Logic in Visual Question Answering with Flowcharts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce FlowVQA, a novel benchmark aimed at assessing the capabilities of visual question-answering multimodal language models in reasoning with flowcharts as visual contexts. |
SHUBHANKAR SINGH et. al. | arxiv-cs.CL | 2024-06-27 |
| 800 | Context Matters: An Empirical Study of The Impact of Contextual Information in Temporal Question Answering Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce two new context-rich TQA datasets, ContextAQA and ContextTQE, and provide comprehensive evaluations and guidelines for training robust TQA models. |
DAN SCHUMACHER et. al. | arxiv-cs.CL | 2024-06-27 |
| 801 | TrustUQA: A Trustful Framework for Unified Structured Data Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose TrustUQA, a trustful QA framework that can simultaneously support multiple types of structured data in a unified way. |
WEN ZHANG et. al. | arxiv-cs.CL | 2024-06-27 |
| 802 | Explicit Diversity Conditions for Effective Question Answer Generation with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present explicit diversity conditions for QAG, focusing on spatial aspects, question types, and entities, substantially increasing diversity in QA generation. |
Vikas Yadav; Hyuk Joon Kwon; Vijay Srinivasan; Hongxia Jin; | arxiv-cs.CL | 2024-06-25 |
| 803 | Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing benchmarks employ irrelevant noise texts to artificially extend the length of test cases, diverging from the real-world scenarios of long-context applications. To bridge this gap, we propose a novel long-context benchmark, Loong, aligning with realistic scenarios through extended multi-document question answering (QA). |
MINZHENG WANG et. al. | arxiv-cs.CL | 2024-06-25 |
| 804 | Advancing Question Answering on Handwritten Documents: A State-of-the-Art Recognition-Based Model for HW-SQuAD Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a novel recognition-based approach that improves upon the previous state-of-the-art on the HW-SQuAD and BenthamQA datasets. |
Aniket Pal; Ajoy Mondal; C. V. Jawahar; | arxiv-cs.CV | 2024-06-25 |
| 805 | Context-augmented Retrieval: A Novel Framework for Fast Information Retrieval Based Response Generation Using Large Language Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For the same, this work proposes a new approach Context Augmented retrieval (CAR), where partitioning of vector database by real-time classification of information flowing into the corpus is done. |
Sai Ganesh; Anupam Purwar; Gautam B; | arxiv-cs.IR | 2024-06-24 |
| 806 | Towards Understanding Contracts Grammar: A Large Language Model-Based Extractive Question-Answering Approach Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Software Engineering (SE) contracts play a pivotal role in Information Technology Outsourcing (ITO) projects. The obligations in SE contracts are known to be a useful source for … |
Gokul Rejithkumar; P. Anish; S. Ghaisas; | 2024 IEEE 32nd International Requirements Engineering … | 2024-06-24 |
| 807 | CogMG: Collaborative Augmentation Between Large Language Model and Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce a collaborative augmentation framework, CogMG, leveraging knowledge graphs to address the limitations of LLMs in QA scenarios, explicitly targeting the problems of incomplete knowledge coverage and knowledge update misalignment. |
Tong Zhou; Yubo Chen; Kang Liu; Jun Zhao; | arxiv-cs.CL | 2024-06-24 |
| 808 | DEXTER: A Benchmark for Open-domain Complex Question Answering Using LLMs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While retrieval performance for classical QA tasks is well explored, their capabilities for heterogeneous complex retrieval tasks, especially in an open-domain setting, and the impact on downstream QA performance, are relatively unexplored. To address this, in this work, we propose a benchmark composing diverse complex QA tasks and provide a toolkit to evaluate state-of-the-art pre-trained dense and sparse retrieval models in an open-domain setting. |
Venktesh V. Deepali Prabhu; Avishek Anand; | arxiv-cs.CL | 2024-06-24 |
| 809 | HCQA @ Ego4D EgoSchema Challenge 2024 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this report, we present our champion solution for Ego4D EgoSchema Challenge in CVPR 2024. |
HAOYU ZHANG et. al. | arxiv-cs.CV | 2024-06-22 |
| 810 | 70B-parameter Large Language Models in Japanese Medical Question-answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here we utilize multiple 70B-parameter LLMs for the first time and show that instruction tuning using Japanese medical question-answering dataset significantly improves the ability of Japanese LLMs to solve Japanese medical license exams, surpassing 50\% in accuracy. |
Issey Sukeda; Risa Kishikawa; Satoshi Kodera; | arxiv-cs.CL | 2024-06-21 |
| 811 | Generate-then-Ground in Retrieval-Augmented Generation for Multi-hop Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the performance of this retrieve-then-read paradigm is constrained by the retriever and the inevitable noise in the retrieved documents. To mitigate these challenges, we introduce a novel generate-then-ground (GenGround) framework, synergizing the parametric knowledge of LLMs and external documents to solve a multi-hop question. |
ZHENGLIANG SHI et. al. | arxiv-cs.CL | 2024-06-21 |
| 812 | Towards Improved Multi-Source Attribution for Long-Form Answer Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite gaining increasing popularity for usage in QA systems and search engines, current LLMs struggle with attribution for long-form responses which require reasoning over multiple evidence sources. To address this, in this paper we aim to improve the attribution capability of LLMs for long-form answer generation to multiple sources, with multiple citations per sentence. |
Nilay Patel; Shivashankar Subramanian; Siddhant Garg; Pratyay Banerjee; Amita Misra; | naacl | 2024-06-20 |
| 813 | End-to-End Beam Retrieval for Multi-Hop Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce Beam Retrieval, an end-to-end beam retrieval framework for multi-hop QA. |
Jiahao Zhang; Haiyang Zhang; Dongmei Zhang; Liu Yong; Shen Huang; | naacl | 2024-06-20 |
| 814 | AudioChatLlama: Towards General-Purpose Speech Abilities for LLMs IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we extend the instruction-tuned Llama-2 model with end-to-end general-purpose speech processing and reasoning abilities while maintaining the wide range of original LLM capabilities, without using any carefully curated paired data. |
YASSIR FATHULLAH et. al. | naacl | 2024-06-20 |
| 815 | Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models Through Question Complexity IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a novel adaptive QA framework that can dynamically select the most suitable strategy for (retrieval-augmented) LLMs from the simplest to the most sophisticated ones based on the query complexity. |
Soyeong Jeong; Jinheon Baek; Sukmin Cho; Sung Ju Hwang; Jong Park; | naacl | 2024-06-20 |
| 816 | Mitigating Bias for Question Answering Models By Tracking Bias Influence Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose BMBI, an approach to mitigate the bias of multiple-choice QA models. |
MINGYU MA et. al. | naacl | 2024-06-20 |
| 817 | CPopQA: Ranking Cultural Concept Popularity By LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the extent to which an LLM effectively captures corpus-level statistical trends of concepts for reasoning, especially long-tail ones, is largely underexplored. In this study, we introduce a novel few-shot question-answering task (CPopQA) that examines LLMs� statistical ranking abilities for long-tail cultural concepts (e. g. , holidays), particularly focusing on these concepts� popularity in the United States and the United Kingdom, respectively. |
Ming Jiang; Mansi Joshi; | naacl | 2024-06-20 |
| 818 | TRAQ: Trustworthy Retrieval Augmented Question Answering Via Conformal Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Retrieval augmented generation (RAG) is a promising strategy to avoid hallucinations, but it does not provide guarantees on its correctness. To address this challenge, we propose the Trustworthy Retrieval Augmented Question Answering, or *TRAQ*, which provides the first end-to-end statistical correctness guarantee for RAG. |
Shuo Li; Sangdon Park; Insup Lee; Osbert Bastani; | naacl | 2024-06-20 |
| 819 | On Narrative Question Answering Skills Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing task-level skill views oversimplify the multidimensional nature of tasks, while question-level taxonomies face issues in evaluation and methodology. To address these challenges, we introduce a more inclusive skill taxonomy that synthesizes and redefines narrative understanding skills from previous taxonomies and includes a generation skill dimension from the answering perspective. |
Emil Kalbaliyev; Kairit Sirts; | naacl | 2024-06-20 |
| 820 | Pregnant Questions: The Importance of Pragmatic Awareness in Maternal Health Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In a high-risk domain such as maternal and infant health, a question-answering system must recognize these pragmatic constraints and go beyond simply answering user questions, examining them in context to respond helpfully. To achieve this, we study assumptions and implications, or pragmatic inferences, made when mothers ask questions about pregnancy and infant care by collecting a dataset of 2,727 inferences from 500 questions across three diverse sources. |
NEHA SRIKANTH et. al. | naacl | 2024-06-20 |
| 821 | Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce a novel framework for enhancing LLMs’ planning capabilities by using planning data derived from knowledge graphs (KGs). |
JUNJIE WANG et. al. | arxiv-cs.CL | 2024-06-20 |
| 822 | QPaug: Question and Passage Augmentation for Open-Domain Question Answering of LLMs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a simple yet efficient method called question and passage augmentation (QPaug) via LLMs for open-domain QA. |
Minsang Kim; Cheoneum Park; Seungjun Baek; | arxiv-cs.CL | 2024-06-20 |
| 823 | TTQA-RS- A Break-down Prompting Approach for Multi-hop Table-Text Question Answering with Reasoning and Summarization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we have proposed a Retrieval Augmented Generation (RAG) based model – TTQA-RS: A break-down prompting approach for Multi-hop Table-Text Question Answering with Reasoning and Summarization. |
Jayetri Bardhan; Bushi Xiao; Daisy Zhe Wang; | arxiv-cs.CL | 2024-06-20 |
| 824 | Self-Prompting Large Language Models for Zero-Shot Open-Domain QA IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a Self-Prompting framework to explicitly utilize the massive knowledge encoded in the parameters of LLMs and their strong instruction understanding abilities. |
Junlong Li; Jinyuan Wang; Zhuosheng Zhang; Hai Zhao; | naacl | 2024-06-20 |
| 825 | SEMQA: Semi-Extractive Multi-Source Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce a new QA task for answering multi-answer questions by summarizing multiple diverse sources in a semi-extractive fashion. |
TAL SCHUSTER et. al. | naacl | 2024-06-20 |
| 826 | Unveiling Divergent Inductive Biases of LLMs on Temporal Data Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Despite the adeptness of Large Language Models (LLMs) in discerning patterns and relationships from data, their inherent comprehension of temporal dynamics remains a formidable challenge. This research meticulously explores these intrinsic challenges within LLMs, with a specific emphasis on evaluating the performance of GPT-3. |
Sindhu Kishore; Hangfeng He; | naacl | 2024-06-20 |
| 827 | PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models As Decision Makers IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we conduct a study to utilize LLMs as a solution for decision making that requires complex data analysis. |
Myeonghwa Lee; Seonho An; Min-Soo Kim; | naacl | 2024-06-20 |
| 828 | LLaSA: A Multimodal LLM for Human Activity Analysis Through Wearable and Smartphone Sensors Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce LLaSA (Large Language and Sensor Assistant), a compact 13B model that enables ask-anything, open-ended question answering grounded in raw IMU data. |
Sheikh Asif Imran; Mohammad Nur Hossain Khan; Subrata Biswas; Bashima Islam; | arxiv-cs.CL | 2024-06-20 |
| 829 | SQATIN: Supervised Instruction Tuning Meets Question Answering for Improved Dialogue NLU Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce SQATIN, a new framework for dialog NLU based on (i) instruction tuning and (ii) question-answering-based formulation of ID and VE tasks. |
Evgeniia Razumovskaia; Goran Glava�; Anna Korhonen; Ivan Vulic; | naacl | 2024-06-20 |
| 830 | SynDARin: Synthesising Datasets for Automated Reasoning in Low-Resource Languages Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This means that producing novel models and measuring the performance of multilingual LLMs in low-resource languages is challenging. To mitigate this, we propose $\textbf{S}$yn$\textbf{DAR}$in, a method for generating and validating QA datasets for low-resource languages. |
Gayane Ghazaryan; Erik Arakelyan; Pasquale Minervini; Isabelle Augenstein; | arxiv-cs.CL | 2024-06-20 |
| 831 | FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we formalize three major desiderata for a fine-grained evaluation of robustness of TQA systems. |
Wei Zhou; Mohsen Mesgar; Heike Adel; Annemarie Friedrich; | naacl | 2024-06-20 |
| 832 | Retrieval Helps or Hurts? A Deeper Dive Into The Efficacy of Retrieval Augmentation to Language Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, our goal is to offer a more detailed, fact-centric analysis by exploring the effects of combinations of entities and relations. |
Seiji Maekawa; Hayate Iso; Sairam Gurajada; Nikita Bhutani; | naacl | 2024-06-20 |
| 833 | Evaluating RAG-Fusion with RAGElo: An Automated Elo-based Framework Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This results in difficulties in evaluating RAG variations, like RAG-Fusion (RAGF), in the context of a product QA task at Infineon Technologies. To solve these problems, we propose a comprehensive evaluation framework, which leverages Large Language Models (LLMs) to generate large datasets of synthetic queries based on real user queries and in-domain documents, uses LLM-as-a-judge to rate retrieved documents and answers, evaluates the quality of answers, and ranks different variants of Retrieval-Augmented Generation (RAG) agents with RAGElo’s automated Elo-based competition. |
Zackary Rackauckas; Arthur Câmara; Jakub Zavrel; | arxiv-cs.IR | 2024-06-20 |
| 834 | Temporal Knowledge Graph Question Answering: A Survey Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work aims to serve as a comprehensive reference for TKGQA and to stimulate further research. |
MIAO SU et. al. | arxiv-cs.CL | 2024-06-20 |
| 835 | Model Internals-based Answer Attribution for Trustworthy Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we present MIRAGE –Model Internals-based RAG Explanations — a plug-and-play approach using model internals for faithful answer attribution in RAG applications. |
Jirui Qi; Gabriele Sarti; Raquel Fernández; Arianna Bisazza; | arxiv-cs.CL | 2024-06-19 |
| 836 | Towards Robust Evaluation: A Comprehensive Taxonomy of Datasets and Metrics for Open Domain Question Answering in The Era of Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a novel taxonomy for ODQA datasets that incorporates both the modality and difficulty of the question types. |
Akchay Srivastava; Atif Memon; | arxiv-cs.CL | 2024-06-19 |
| 837 | From RAGs to Rich Parameters: Probing How Language Models Utilize External Knowledge Over Parametric Information for Factual Queries Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we mechanistically examine the RAG pipeline to highlight that language models take shortcut and have a strong bias towards utilizing only the context information to answer the question, while relying minimally on their parametric memory. |
HITESH WADHWA et. al. | arxiv-cs.CL | 2024-06-18 |
| 838 | Problem-Solving in Language Model Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To improve the reasoning and question-answering capabilities of Large Language Models (LLMs), several multi-agent approaches have been introduced. |
Ciaran Regan; Alexandre Gournail; Mizuki Oka; | arxiv-cs.AI | 2024-06-18 |
| 839 | On The Robustness of Language Models for Tabular Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We highlight the need for improved methodologies, including structure-aware self-attention mechanisms and better handling of domain-specific tabular data, to develop more reliable LLMs for table comprehension. |
Kushal Raj Bhandari; Sixue Xing; Soham Dan; Jianxi Gao; | arxiv-cs.CL | 2024-06-18 |
| 840 | QRMeM: Unleash The Length Limitation Through Question Then Reflection Memory Mechanism Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing techniques face challenges with static knowledge integration, leading to insufficient adaptation to task-specific needs and missing multi-segmentation relationships, which hinders the dynamic reorganization and logical combination of relevant segments during the response process. To address these issues, we introduce a novel strategy, Question then Reflection Memory Mechanism (QRMeM), incorporating a dual-structured memory pool. |
BO WANG et. al. | arxiv-cs.CL | 2024-06-18 |
| 841 | Diversify, Rationalize, and Combine: Ensembling Multiple QA Strategies for Zero-shot Knowledge-based VQA Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose Diversification, Evidence Truncation, and Combination for Knowledge-based Elucidation (DietCoke), which utilizes a bundle of complementary question-answering tactics and aggregates their answers using textual rationales. |
Miaoyu Li; Haoxin Li; Zilin Du; Boyang Li; | arxiv-cs.CL | 2024-06-18 |
| 842 | AvaTaR: Optimizing LLM Agents for Tool Usage Via Contrastive Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Here, we introduce AvaTaR, a novel and automated framework that optimizes an LLM agent to effectively leverage provided tools, improving performance on a given task. |
SHIRLEY WU et. al. | arxiv-cs.LG | 2024-06-17 |
| 843 | RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To foster sound evaluation of language models, we introduce a new test dataset named RepLiQA, suited for question-answering and topic retrieval tasks. |
JOAO MONTEIRO et. al. | arxiv-cs.CL | 2024-06-17 |
| 844 | TRACE The Evidence: Constructing Knowledge-Grounded Reasoning Chains for Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To enhance the multi-hop reasoning ability of RAG models, we propose TRACE. |
Jinyuan Fang; Zaiqiao Meng; Craig Macdonald; | arxiv-cs.CL | 2024-06-17 |
| 845 | Multi-LLM QA with Embodied Exploration Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: There is a lack of insight into whether a Multi-LLM system can handle question-answering based on observations from embodied exploration. In this work, we address this gap by investigating the use of Multi-Embodied LLM Explorers (MELE) for QA in an unknown environment. |
Bhrij Patel; Vishnu Sashank Dorbala; Amrit Singh Bedi; Dinesh Manocha; | arxiv-cs.LG | 2024-06-16 |
| 846 | Beyond Raw Videos: Understanding Edited Videos with Large Multimodal Model Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we leverage the edited videos on a popular short video platform, \textit{i.e.}, TikTok, and build a video VQA benchmark (named EditVid-QA) covering four typical editing categories, i.e., effect, funny, meme, and game. |
LU XU et. al. | arxiv-cs.CV | 2024-06-14 |
| 847 | EWEK-QA: Enhanced Web and Efficient Knowledge Graph Retrieval for Citation-based Question Answering Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Second, web-retrieved contents are usually obtained by some simple heuristics such as fixed length or breakpoints which might lead to splitting information into pieces. To mitigate these issues, we propose our enhanced web and efficient knowledge graph (KG) retrieval solution (EWEK-QA) to enrich the content of the extracted knowledge fed to the system. |
MOHAMMAD DEHGHAN et. al. | arxiv-cs.CL | 2024-06-14 |
| 848 | Datasets for Multilingual Answer Sentence Selection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce new high-quality datasets for AS2 in five European languages (French, German, Italian, Portuguese, and Spanish), obtained through supervised Automatic Machine Translation (AMT) of existing English AS2 datasets such as ASNQ, WikiQA, and TREC-QA using a Large Language Model (LLM). |
Matteo Gabburo; Stefano Campese; Federico Agostini; Alessandro Moschitti; | arxiv-cs.CL | 2024-06-14 |
| 849 | Precision Empowers, Excess Distracts: Visual Question Answering With Dynamically Infused Knowledge In Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce an approach for KBVQA, augmenting the existing vision-language transformer encoder-decoder (OFA) model. |
Manas Jhalani; Annervaz K M; Pushpak Bhattacharyya; | arxiv-cs.CL | 2024-06-14 |
| 850 | VTQA: Visual Text Question Answering Via Entity Alignment and Cross-Media Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Motivated by the need for a more comprehensive evaluation we introduce a novel dataset comprising 23781 questions derived from 10124 image-text pairs. |
Kang Chen; Xiangqian Wu; | cvpr | 2024-06-13 |
| 851 | CoG-DQA: Chain-of-Guiding Learning with Large Language Models for Diagram Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we introduce the Chain-of-Guiding Learning Model for Diagram Question Answering (CoG-DQA) a novel framework that effectively addresses DQA challenges. |
SHAOWEI WANG et. al. | cvpr | 2024-06-13 |
| 852 | Language-aware Visual Semantic Distillation for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we are inspired by the human recognition and learning pattern and propose VideoDistill a framework with language-aware (i.e. goal-driven) behavior in both vision perception and answer generation process. |
Bo Zou; Chao Yang; Yu Qiao; Chengbin Quan; Youjian Zhao; | cvpr | 2024-06-13 |
| 853 | Optimizing Visual Question Answering Models for Driving: Bridging The Gap Between Human and Machine Attention Patterns Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose an approach integrating filters to optimize the model’s attention mechanisms, prioritizing relevant objects and improving accuracy. |
Kaavya Rekanar; Martin Hayes; Ganesh Sistu; Ciaran Eising; | arxiv-cs.CV | 2024-06-13 |
| 854 | DIEM: Decomposition-Integration Enhancing Multimodal Insights Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we propose the Decomposition-Integration Enhancing Multimodal Insight (DIEM) which initially decomposes the given question and image into multiple subquestions and several sub-images aiming to isolate specific elements for more focused analysis. |
XINYI JIANG et. al. | cvpr | 2024-06-13 |
| 855 | Ranking Distillation for Open-Ended Video Question Answering with Insufficient Labels Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As a result existing works tend to directly treat all the unlabeled answers as negative labels leading to limited ability for generalization. In this work we introduce a simple yet effective ranking distillation framework (RADI) to mitigate this problem without additional manual annotation. |
Tianming Liang; Chaolei Tan; Beihao Xia; Wei-Shi Zheng; Jian-Fang Hu; | cvpr | 2024-06-13 |
| 856 | Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Here we propose a beam-search-based most-likely prediction and a temperature-based multimodal prediction to implement both deterministic and stochastic inferences. |
Inhwan Bae; Junoh Lee; Hae-Gon Jeon; | cvpr | 2024-06-13 |
| 857 | Synthesize Step-by-Step: Tools Templates and LLMs As Data Generators for Reasoning-Based Chart VQA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work we address the lack of reasoning ability by data augmentation. |
Zhuowan Li; Bhavan Jasani; Peng Tang; Shabnam Ghadar; | cvpr | 2024-06-13 |
| 858 | Too Many Frames, Not All Useful: Efficient Strategies for Long-Form Video QA Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Such VLMs often independently caption a large number of frames uniformly sampled from long videos, which is not efficient and can mostly be redundant. Questioning these decision choices, we explore optimal strategies for key-frame selection that can significantly reduce these redundancies, namely Hierarchical Keyframe Selector. |
JONGWOO PARK et. al. | arxiv-cs.CV | 2024-06-13 |
| 859 | Consistency and Uncertainty: Identifying Unreliable Responses From Black-Box Vision-Language Models for Selective Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose using the principle of neighborhood consistency to identify unreliable responses from a black-box vision-language model in question answering tasks. |
Zaid Khan; Yun Fu; | cvpr | 2024-06-13 |
| 860 | Can I Trust Your Answer? Visually Grounded Video Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Experiments with different backbones demonstrate that this grounding mechanism improves both grounding and QA. With these efforts we aim to push towards trustworthy VLMs in VQA systems. |
Junbin Xiao; Angela Yao; Yicong Li; Tat-Seng Chua; | cvpr | 2024-06-13 |
| 861 | On Scaling Up A Multilingual Vision and Language Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We explore the boundaries of scaling up a multilingual vision and language model both in terms of size of the components and the breadth of its training task mixture. |
XI CHEN et. al. | cvpr | 2024-06-13 |
| 862 | Towards Multilingual Audio-Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we work towards extending Audio-Visual Question Answering (AVQA) to multilingual settings. |
ORCHID CHETIA PHUKAN et. al. | arxiv-cs.LG | 2024-06-13 |
| 863 | Grounded Question-Answering in Long Egocentric Videos IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper we delve into open-ended question-answering (QA) in long egocentric videos which allows individuals or robots to inquire about their own past visual experiences. |
Shangzhe Di; Weidi Xie; | cvpr | 2024-06-13 |
| 864 | Multi-Factor Adaptive Vision Selection for Egocentric Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The challenge of interpreting the world from a human perspective in Artificial Intelligence (AI) is particularly evident in egocentric video question answering, which grapples with issues like small object recognition, noise suppression, and spatial-temporal reasoning. To address these challenges, we introduce the Multi-Factor Adaptive vision Selection (MFAS) framework. |
HAOYU ZHANG et. al. | icml | 2024-06-12 |
| 865 | TroVE: Inducing Verifiable and Efficient Toolboxes for Solving Programmatic Tasks IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present TROVE, a training-free method of inducing a verifiable and efficient toolbox of functions, by generating via using, growing, and periodically trimming the toolbox. |
Zhiruo Wang; Graham Neubig; Daniel Fried; | icml | 2024-06-12 |
| 866 | In-Context Principle Learning from Mistakes IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Nonetheless, all ICL-based approaches only learn from correct input-output pairs. In this paper, we revisit this paradigm, by learning more from the few given input-output examples. |
TIANJUN ZHANG et. al. | icml | 2024-06-12 |
| 867 | Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we suggest investigating internal activations and quantifying LLM’s truthfulness using the local intrinsic dimension (LID) of model activations. |
Fan Yin; Jayanth Srinivasa; Kai-Wei Chang; | icml | 2024-06-12 |
| 868 | Unifying Image Processing As Visual Prompting Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these advances have predominantly concentrated on high-level vision tasks, with less attention paid to low-level vision tasks. To address this issue, we propose a universal model for general image processing that covers image restoration, image enhancement, image feature extraction tasks, etc. |
YIHAO LIU et. al. | icml | 2024-06-12 |
| 869 | Switchable Decision: Dynamic Neural Generation Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a switchable decision to accelerate inference by dynamically assigning computation resources for each data instance. |
Shujian Zhang; Korawat Tanwisuth; Chengyue Gong; Pengcheng He; Mingyuan Zhou; | icml | 2024-06-12 |
| 870 | DARA: Decomposition-Alignment-Reasoning Autonomous Language Agent for Question Answering Over Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To improve the neural-symbolic reasoning capabilities of language agents powered by Large Language Models (LLMs) in KGQA, we propose the DecompositionAlignment-Reasoning Agent (DARA) framework. |
Haishuo Fang; Xiaodan Zhu; Iryna Gurevych; | arxiv-cs.CL | 2024-06-11 |
| 871 | Question-Answering (QA) Model for A Personalized Learning Assistant for Arabic Language Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: This paper describes the creation, optimization, and assessment of a question-answering (QA) model for a personalized learning assistant that uses BERT transformers customized for … |
Mohammad Sammoudi; Ahmad Habaybeh; Huthaifa I. Ashqar; Mohammed Elhenawy; | ArXiv | 2024-06-11 |
| 872 | DR-RAG: Applying Dynamic Document Relevance to Retrieval-Augmented Generation for Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To mine the relevance, a two-stage retrieval framework called Dynamic-Relevant Retrieval-Augmented Generation (DR-RAG) is proposed to improve document retrieval recall and the accuracy of answers while maintaining efficiency. |
ZIJIAN HEI et. al. | arxiv-cs.LG | 2024-06-11 |
| 873 | Scholarly Question Answering Using Large Language Models in The NFDI4DataScience Gateway Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces a scholarly Question Answering (QA) system on top of the NFDI4DataScience Gateway, employing a Retrieval Augmented Generation-based (RAG) approach. |
HAMED BABAEI GIGLOU et. al. | arxiv-cs.CL | 2024-06-11 |
| 874 | Situational Awareness Matters in 3D Vision Language Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Being able to carry out complicated vision language reasoning tasks in 3D space represents a significant milestone in developing household robots and human-centered embodied AI. In this work, we demonstrate that a critical and distinct challenge in 3D vision language reasoning is situational awareness, which incorporates two key components: (1) The autonomous agent grounds its self-location based on a language prompt. |
Yunze Man; Liang-Yan Gui; Yu-Xiong Wang; | arxiv-cs.CV | 2024-06-11 |
| 875 | Benchmarking Vision-Language Contrastive Methods for Medical Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Through this study, we aim to answer the following research questions: (i) How transferable are general-domain representations to the medical domain? |
SHUVENDU ROY et. al. | arxiv-cs.CV | 2024-06-11 |
| 876 | VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present the VideoLLaMA 2, a set of Video Large Language Models (Video-LLMs) designed to enhance spatial-temporal modeling and audio understanding in video and audio-oriented tasks. |
ZESEN CHENG et. al. | arxiv-cs.CV | 2024-06-11 |
| 877 | MemoriQA: A Question-Answering Lifelog Dataset Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Lifelogging can be referred to as the process of passively collecting data on an individual’s daily life. Lifelog data provides a large amount of information which can be used to … |
Quang-Linh Tran; Binh T. Nguyen; Gareth J. F. Jones; C. Gurrin; | Proceedings of the 1st ACM Workshop on AI-Powered Q&A … | 2024-06-10 |
| 878 | Chart Question Answering Based on Modality Conversion and Large Language Models Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: A two-stage chart question answering system is proposed in this paper. Chart/plot images are first converted into structured text-based data by a transformer-based conversion … |
Yi-Cheng Liu; Wei-Ta Chu; | Proceedings of the 1st ACM Workshop on AI-Powered Q&A … | 2024-06-10 |
| 879 | Evaluating The Retrieval Component in LLM-Based Question Answering Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study proposes a straightforward baseline for evaluating retrievers in Retrieval-Augmented Generation (RAG)-based chatbots. |
Ashkan Alinejad; Krtin Kumar; Ali Vahdat; | arxiv-cs.CL | 2024-06-10 |
| 880 | MedExQA: Medical Question Answering Benchmark with Multiple Explanations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces MedExQA, a novel benchmark in medical question-answering, to evaluate large language models’ (LLMs) understanding of medical knowledge through explanations. |
Yunsoo Kim; Jinge Wu; Yusuf Abdulle; Honghan Wu; | arxiv-cs.CL | 2024-06-10 |
| 881 | MyEachtraX: Lifelog Question Answering on Mobile Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Your whole life in your pocket. That is the premise of lifelogging, a technology that captures and stores every moment of your life in digital form. Built on top of MyEachtra and … |
Ly-Duyen Tran; Thanh-Binh Nguyen; C. Gurrin; Liting Zhou; | Proceedings of the 7th Annual ACM Workshop on the Lifelog … | 2024-06-10 |
| 882 | MedREQAL: Examining Medical Knowledge Recall of Large Language Models Via Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we examine the capability of LLMs to exhibit medical knowledge recall by constructing a novel dataset derived from systematic reviews — studies synthesizing evidence-based answers for specific medical questions. |
Juraj Vladika; Phillip Schneider; Florian Matthes; | arxiv-cs.CL | 2024-06-09 |
| 883 | Zero-Shot End-To-End Spoken Question Answering In Medical Domain Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our study introduces a novel zero-shot SQA approach, compared to traditional cascade systems. |
Yanis Labrak; Adel Moumen; Richard Dufour; Mickael Rouvier; | arxiv-cs.CL | 2024-06-09 |
| 884 | CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: More importantly, although these datasets often extend their linguistic range via translation or some other approaches, they usually keep images the same, resulting in narrow cultural representation. To address these limitations, we construct CVQA, a new Culturally-diverse multilingual Visual Question Answering benchmark, designed to cover a rich set of languages and cultures, where we engage native speakers and cultural experts in the data collection process. |
DAVID ROMERO et. al. | arxiv-cs.CV | 2024-06-09 |
| 885 | MrRank: Improving Question Answering Retrieval System Through Multi-Result Ranking Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose an approach that leverages learning-to-rank techniques to combine heterogeneous IR systems. |
Danupat Khamnuansin; Tawunrat Chalothorn; Ekapol Chuangsuwanich; | arxiv-cs.CL | 2024-06-09 |
| 886 | LinkQ: An LLM-Assisted Visual Interface for Knowledge Graph Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present LinkQ, a system that leverages a large language model (LLM) to facilitate knowledge graph (KG) query construction through natural language question-answering. |
Harry Li; Gabriel Appleby; Ashley Suh; | arxiv-cs.CL | 2024-06-07 |
| 887 | CRAG – Comprehensive RAG Benchmark IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Retrieval-Augmented Generation (RAG) has recently emerged as a promising solution to alleviate Large Language Model (LLM)’s deficiency in lack of knowledge. Existing RAG datasets, … |
XIAO YANG et. al. | ArXiv | 2024-06-07 |
| 888 | MATTER: Memory-Augmented Transformer Using Heterogeneous Knowledge Sources Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce an efficient memory-augmented transformer called MATTER, designed to retrieve relevant knowledge from multiple heterogeneous knowledge sources. |
Dongkyu Lee; Chandana Satya Prakash; Jack FitzGerald; Jens Lehmann; | arxiv-cs.CL | 2024-06-07 |
| 889 | CRAG — Comprehensive RAG Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing RAG datasets, however, do not adequately represent the diverse and dynamic nature of real-world Question Answering (QA) tasks. To bridge this gap, we introduce the Comprehensive RAG Benchmark (CRAG), a factual question answering benchmark of 4,409 question-answer pairs and mock APIs to simulate web and Knowledge Graph (KG) search. |
XIAO YANG et. al. | arxiv-cs.CL | 2024-06-07 |
| 890 | TCMD: A Traditional Chinese Medicine QA Dataset for Evaluating Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a new medical question-answering (QA) dataset that contains massive manual instruction for solving Traditional Chinese Medicine examination tasks, called TCMD. |
Ping Yu; Kaitao Song; Fengchen He; Ming Chen; Jianfeng Lu; | arxiv-cs.CL | 2024-06-07 |
| 891 | ComplexTempQA: A Large-Scale Dataset for Complex Temporal Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce ComplexTempQA, a large-scale dataset consisting of over 100 million question-answer pairs designed to tackle the challenges in temporal question answering. |
Raphael Gruber; Abdelrahman Abdallah; Michael Färber; Adam Jatowt; | arxiv-cs.CL | 2024-06-07 |
| 892 | FairytaleQA Translated: Enabling Educational Question and Answer Generation in Less-Resourced Languages Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While numerous datasets have been developed in English for this purpose, a noticeable void exists in less-resourced languages. To alleviate this gap, our paper introduces machine-translated versions of FairytaleQA, a renowned QA dataset designed to assess and enhance narrative comprehension skills in young children. |
Bernardo Leite; Tomás Freitas Osório; Henrique Lopes Cardoso; | arxiv-cs.CL | 2024-06-06 |
| 893 | Measuring Retrieval Complexity in Question Answering Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate which questions are challenging for retrieval-based Question Answering (QA). |
Matteo Gabburo; Nicolaas Paul Jedema; Siddhant Garg; Leonardo F. R. Ribeiro; Alessandro Moschitti; | arxiv-cs.CL | 2024-06-05 |
| 894 | I’ve Got The Answer! Interpretation of LLMs Hidden States in Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We also identify the layers which have a negative effect on the model’s behavior. As a prospect of practical application of the hypothesis, we propose to train such weak layers additionally in order to improve the quality of the task solution. |
Valeriya Goloviznina; Evgeny Kotelnikov; | arxiv-cs.CL | 2024-06-04 |
| 895 | UniOQA: A Unified Framework for Knowledge Graph Question Answering with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce UniOQA, a unified framework that integrates two complementary parallel workflows. |
Zhuoyang Li; Liran Deng; Hui Liu; Qiaoqiao Liu; Junzhao Du; | arxiv-cs.CL | 2024-06-04 |
| 896 | Translation Deserves Better: Analyzing Translation Artifacts in Cross-lingual Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We find that these artifacts can significantly affect the models, confirmed by extensive experiments across diverse models, languages, and translation processes. In light of this, we present a simple data augmentation strategy that can alleviate the adverse impacts of translation artifacts. |
CHAEHUN PARK et. al. | arxiv-cs.CL | 2024-06-04 |
| 897 | MedFuzz: Exploring The Robustness of Large Language Models in Medical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we present an adversarial method that we call MedFuzz (for medical fuzzing). |
ROBERT OSAZUWA NESS et. al. | arxiv-cs.CL | 2024-06-03 |
| 898 | Large Language Models for Sexual, Reproductive, and Maternal Health Rights* Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: This research explores the potential of Large Language Models in the context of healthcare solutions, with a specific focus on Sexual, Reproduc-tive, and Maternal Health Rights … |
W. SEWUNETIE et. al. | 2024 IEEE 12th International Conference on Healthcare … | 2024-06-03 |
| 899 | Seeing Beyond Borders: Evaluating LLMs in Multilingual Ophthalmological Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Large Language Models (LLMs), such as GPT-3.5 [1] and GPT-4 [2], have significant potential for transforming several aspects of patient care from clinical note summarization to … |
DAVID RESTREPO et. al. | 2024 IEEE 12th International Conference on Healthcare … | 2024-06-03 |
| 900 | Selectively Answering Visual Questions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose Avg BLEU, a calibration score combining the benefits of both sampling and likelihood methods across modalities. |
Julian Martin Eisenschlos; Hernán Maina; Guido Ivetta; Luciana Benotti; | arxiv-cs.CL | 2024-06-03 |
| 901 | Compositional 4D Dynamic Scenes Understanding with Physics Priors for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce DynSuperCLEVR, the first video question answering dataset that focuses on language understanding of the dynamic properties of 3D objects. |
XINGRUI WANG et. al. | arxiv-cs.CV | 2024-06-02 |
| 902 | Beyond Boundaries: A Human-like Approach for Question Answering Over Structured and Unstructured Information Sources Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Answering factual questions from heterogenous sources, such as graphs and text, is a key capacity of intelligent systems. Current approaches either (i) perform question answering … |
Jens Lehmann; Dhananjay Bhandiwad; Preetam Gattogi; S. Vahdati; | Transactions of the Association for Computational … | 2024-06-01 |
| 903 | SPAGHETTI: Open-Domain Question Answering from Heterogeneous Data Sources with Retrieval and Semantic Parsing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce SPAGHETTI: Semantic Parsing Augmented Generation for Hybrid English information from Text Tables and Infoboxes, a hybrid question-answering (QA) pipeline that utilizes information from heterogeneous knowledge sources, including knowledge base, text, tables, and infoboxes. |
HEIDI C. ZHANG et. al. | arxiv-cs.CL | 2024-06-01 |
| 904 | Mix-tower: Light Visual Question Answering Framework Based on Exclusive Self-attention Mechanism Related Papers Related Patents Related Grants Related Venues Related Experts View |
Deguang Chen; Jianrui Chen; Luheng Yang; Fanhua Shang; | Neurocomputing | 2024-06-01 |
| 905 | The Effect of Clustering Algorithms on Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
Rana Husni AlMahmoud; Marwah Alian; | Expert Syst. Appl. | 2024-06-01 |
| 906 | Heterogeneous Interactive Graph Network for Audio-Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
Yihan Zhao; Wei Xi; Gairui Bai; Xinhui Liu; Jizhong Zhao; | Knowl. Based Syst. | 2024-06-01 |
| 907 | Passage-specific Prompt Tuning for Passage Reranking in Question Answering with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose passage-specific prompt tuning for reranking in open-domain question answering (PSPT): a parameter-efficient method that fine-tunes learnable passage-specific soft prompts, incorporating passage-specific knowledge from a limited set of question-passage relevance pairs. |
Xuyang Wu; Zhiyuan Peng; Krishna Sravanthi Rajanala Sai; Hsin-Tai Wu; Yi Fang; | arxiv-cs.CL | 2024-05-31 |
| 908 | Long-Span Question-Answering: Automatic Question Generation and QA-System Ranking Via Side-by-Side Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a holistic pipeline for automatic data generation including question generation, answering, and model scoring using an “Evaluator”. |
BERND BOHNET et. al. | arxiv-cs.CL | 2024-05-31 |
| 909 | QAVidCap: Enhancing Video Captioning Through Question Answering Techniques Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Video captioning is the task of describing video content using natural sentences. While recent models have shown significant improvements in metrics, there are still some … |
Hui Liu; Xiaojun Wan; | Proceedings of the 2024 International Conference on … | 2024-05-30 |
| 910 | GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce GNN-RAG, a novel method for combining language understanding abilities of LLMs with the reasoning abilities of GNNs in a retrieval-augmented generation (RAG) style. |
Costas Mavromatis; George Karypis; | arxiv-cs.CL | 2024-05-30 |
| 911 | Video Question Answering for People with Visual Impairments Using An Egocentric 360-Degree Camera Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper addresses the daily challenges encountered by visually impaired individuals, such as limited access to information, navigation difficulties, and barriers to social interaction. To alleviate these challenges, we introduce a novel visual question answering dataset. |
Inpyo Song; Minjun Joo; Joonhyung Kwon; Jangwon Lee; | arxiv-cs.CV | 2024-05-30 |
| 912 | The First ACM Workshop on AI-Powered Question Answering Systems for Multimedia Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The advent of large language models (LLMs) has energised research in Question-Answering (QA) tasks, enabling responses across varied domains like economics and mathematics. … |
TAI TAN MAI et. al. | Proceedings of the 2024 International Conference on … | 2024-05-30 |
| 913 | Evaluating Zero-Shot GPT-4V Performance on 3D Visual Question Answering Benchmarks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As interest in reformulating the 3D Visual Question Answering (VQA) problem in the context of foundation models grows, it is imperative to assess how these new paradigms influence existing closed-vocabulary datasets. In this case study, we evaluate the zero-shot performance of foundational models (GPT-4 Vision and GPT-4) on well-established 3D VQA benchmarks, namely 3D-VQA and ScanQA. |
Simranjit Singh; Georgios Pavlakos; Dimitrios Stamoulis; | arxiv-cs.CV | 2024-05-29 |
| 914 | MathChat: Benchmarking Mathematical Reasoning and Instruction Following in Multi-Turn Interactions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces MathChat, a comprehensive benchmark specifically designed to evaluate LLMs across a broader spectrum of mathematical tasks. |
ZHENWEN LIANG et. al. | arxiv-cs.AI | 2024-05-29 |
| 915 | A Multi-Source Retrieval Question Answering Framework Based on RAG Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing RAG paradigms are inevitably influenced by erroneous retrieval information, thereby reducing the reliability and correctness of generated results. Therefore, to improve the relevance of retrieval information, this study proposes a method that replaces traditional retrievers with GPT-3.5, leveraging its vast corpus knowledge to generate retrieval information. |
RIDONG WU et. al. | arxiv-cs.IR | 2024-05-29 |
| 916 | Peering Into The Mind of Language Models: An Approach for Attribution in Contextual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce a novel method for attribution in contextual question answering, leveraging the hidden state representations of LLMs. |
Anirudh Phukan; Shwetha Somasundaram; Apoorv Saxena; Koustava Goswami; Balaji Vasan Srinivasan; | arxiv-cs.CL | 2024-05-28 |
| 917 | Conv-CoA: Improving Open-domain Question Answering in Large Language Models Via Conversational Chain-of-Action Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a Conversational Chain-of-Action (Conv-CoA) framework for Open-domain Conversational Question Answering (OCQA). |
Zhenyu Pan; Haozheng Luo; Manling Li; Han Liu; | arxiv-cs.CL | 2024-05-28 |
| 918 | THREAD: Thinking Deeper with Recursive Spawning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Large language models (LLMs) have shown impressive capabilities across diverse settings, but still struggle as the length and complexity of the context increases. To address this challenge, we propose Thinking Recursively and Dynamically (ThReaD). |
Philip Schroeder; Nathaniel Morgan; Hongyin Luo; James Glass; | arxiv-cs.CL | 2024-05-27 |
| 919 | Aligning LLMs Through Multi-perspective User Preference Ranking-based Feedback for Programming Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Code Community Question Answering (CCQA) seeks to tackle programming-related issues, thereby boosting productivity in both software engineering and academic research. Recent … |
HONGYU YANG et. al. | ArXiv | 2024-05-27 |
| 920 | Map-based Modular Approach for Zero-shot Embodied Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents a map-based modular approach to EQA, enabling real-world robots to explore and map unknown environments. |
Koya Sakamoto; Daichi Azuma; Taiki Miyanishi; Shuhei Kurita; Motoaki Kawanabe; | arxiv-cs.RO | 2024-05-26 |
| 921 | Accurate and Nuanced Open-QA Evaluation Through Textual Entailment Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose to study the entailment relations of answers to identify more informative and more general system answers, offering a much closer evaluation to human judgment on both NaturalQuestions and TriviaQA while being learning-free. |
Peiran Yao; Denilson Barbosa; | arxiv-cs.CL | 2024-05-26 |
| 922 | Crafting Interpretable Embeddings By Asking LLMs Questions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce question-answering embeddings (QA-Emb), embeddings where each feature represents an answer to a yes/no question asked to an LLM. |
VINAMRA BENARA et. al. | arxiv-cs.CL | 2024-05-26 |
| 923 | Efficient Medical Question Answering with Knowledge-Augmented Question Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce a method to improve the proficiency of a small language model in the medical domain by employing a two-fold approach. |
JULIEN KHLAUT et. al. | arxiv-cs.CL | 2024-05-23 |
| 924 | Experimental Design of Extractive Question-Answering Systems: Influence of Error Scores and Answer Length Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Question-answering (QA) systems are becoming more and more important because they enable human-computer communication in a natural language. In recent years, significant progress … |
Amer Farea; Frank Emmert-Streib; | J. Artif. Intell. Res. | 2024-05-23 |
| 925 | LOVA3: Learning to Visual Question Answering, Asking and Assessment Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Current Multimodal Large Language Models (MLLMs) primarily focus on question answering, often neglecting the full potential of questioning and assessment skills. Inspired by the human learning mechanism, we introduce LOVA3, an innovative framework named Learning tO Visual question Answering, Asking and Assessment, designed to equip MLLMs with these additional capabilities. |
Henry Hengyuan Zhao; Pan Zhou; Difei Gao; Zechen Bai; Mike Zheng Shou; | arxiv-cs.CV | 2024-05-23 |
| 926 | MentalQA: An Annotated Arabic Corpus for Questions and Answers of Mental Healthcare Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce MentalQA, a novel Arabic dataset featuring conversational-style question-and-answer (QA) interactions. |
Hassan Alhuzali; Ashwag Alasmari; Hamad Alsaleh; | arxiv-cs.CL | 2024-05-21 |
| 927 | MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we tackle multilingual TEC-VQA by introducing MTVQA, the first benchmark featuring high-quality human expert annotations across 9 diverse languages, consisting of 6,778 question-answer pairs across 2,116 images. |
JINGQUN TANG et. al. | arxiv-cs.CV | 2024-05-20 |
| 928 | Causal Event Graph-Guided Language-based Spatiotemporal Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Large Language Models have excelled at encoding and leveraging language patterns in large text-based corpora for various tasks, including spatiotemporal event-based question … |
KAUSHIK ROY et. al. | AAAI Spring Symposia | 2024-05-20 |
| 929 | Increasing The LLM Accuracy for Question Answering: Ontologies to The Rescue! IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Building on the observations of our previous research where the inaccurate LLM-generated SPARQL queries followed incorrect paths, we present an approach that consists of 1) Ontology-based Query Check (OBQC): detects errors by leveraging the ontology of the knowledge graph to check if the LLM-generated SPARQL query matches the semantic of ontology and 2) LLM Repair: use the error explanations with an LLM to repair the SPARQL query. |
Dean Allemang; Juan Sequeda; | arxiv-cs.AI | 2024-05-19 |
| 930 | MemeMQA: Multimodal Question Answering for Memes Via Rationale-Based Inferencing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To extend this research, we introduce MemeMQA, a multimodal question-answering framework aiming to solicit accurate responses to structured questions while providing coherent explanations. |
Siddhant Agarwal; Shivam Sharma; Preslav Nakov; Tanmoy Chakraborty; | arxiv-cs.CL | 2024-05-18 |
| 931 | StackOverflowVQA: Stack Overflow Visual Question Answering Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we focus on the questions which need the understanding of images in addition to the question itself. |
Motahhare Mirzaei; Mohammad Javad Pirhadi; Sauleh Eetemadi; | arxiv-cs.CV | 2024-05-17 |
| 932 | Exploring The Impact of ChatGPT on Wikipedia Engagement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we explore Wikipedia user metrics across four areas: page views, unique visitor numbers, edit counts and editor numbers within twelve language instances of Wikipedia. |
Neal Reeves; Wenjie Yin; Elena Simperl; | arxiv-cs.HC | 2024-05-16 |
| 933 | Question Answering System with Text Mining and Deep Networks Related Papers Related Patents Related Grants Related Venues Related Experts View |
Hüseyin Avni Ardaç; P. Erdoğmuş; | Evol. Syst. | 2024-05-16 |
| 934 | SciQAG: A Framework for Auto-Generated Science Question Answering Dataset with Fine-grained Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce SciQAG, a novel framework for automatically generating high-quality science question-answer pairs from a large corpus of scientific literature based on large language models (LLMs). |
YUWEI WAN et. al. | arxiv-cs.CL | 2024-05-16 |
| 935 | FinTextQA: A Dataset for Long-form Financial Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work introduces FinTextQA, a novel dataset for long-form question answering (LFQA) in finance. |
JIAN CHEN et. al. | arxiv-cs.CL | 2024-05-16 |
| 936 | Towards Better Question Generation in QA-based Event Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, in QA-based EE, the quality of the questions dramatically affects the extraction accuracy, and how to generate high-quality questions for QA-based EE remains a challenge. In this work, to tackle this challenge, we suggest four criteria to evaluate the quality of a question and propose a reinforcement learning method, RLQG, for QA-based EE that can generate generalizable, high-quality, and context-dependent questions and provides clear guidance to QA models. |
Zijin Hong; Jian Liu; | arxiv-cs.CL | 2024-05-16 |
| 937 | Prompting-based Synthetic Data Generation for Few-Shot Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: With this motivation, we show that using large language models can improve Question Answering performance on various datasets in the few-shot setting compared to state-of-the-art approaches. For this, we perform data generation leveraging the Prompting framework, suggesting that language models contain valuable task-agnostic knowledge that can be used beyond the common pre-training/fine-tuning scheme. |
Maximilian Schmidt; Andrea Bartezzaghi; Ngoc Thang Vu; | arxiv-cs.CL | 2024-05-15 |
| 938 | A Knowledge-Injected Curriculum Pretraining Framework for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, in this paper, we propose a general K nowledge-I njected C urriculum P retraining framework (KICP) to achieve comprehensive KG learning and exploitation for KBQA tasks, which is composed of knowledge injection (KI), knowledge adaptation (KA) and curriculum reasoning (CR). |
XIN LIN et. al. | www | 2024-05-13 |
| 939 | TIQ: A Benchmark for Temporal Question Answering with Implicit Time Constraints Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Temporal question answering (QA) involves explicit (e.g., …before 2024) or implicit (e.g., …during the Cold War period) time constraints. Implicit constraints are more … |
Zhen Jia; Philipp Christmann; G. Weikum; | Companion Proceedings of the ACM on Web Conference 2024 | 2024-05-13 |
| 940 | Demonstration of FeVisQA: Free-Form Question Answering Over Data Visualization Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Question Answering (QA) systems playa vital role in knowledge acquisition. CodeQA refers to question answering (QA) over source code for code comprehension purpose. However, … |
Yuanfeng Song; Jinwei Lu; Xuefang Zhao; Raymond Chi-Wing Wong; Haodi Zhang; | 2024 IEEE 40th International Conference on Data Engineering … | 2024-05-13 |
| 941 | Causal Question Answering with Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Hence, in this paper, we aim to answer causal questions with a causality graph, a large-scale dataset of causal relations between noun phrases along with the relations’ provenance data. |
Lukas Bl\{u}baum; Stefan Heindorf; | www | 2024-05-13 |
| 942 | KET-QA: A Dataset for Knowledge Enhanced Table Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to use a knowledge base (KB) as the external knowledge source for TableQA and construct a dataset KET-QA with fine-grained gold evidence annotation. |
Mengkang Hu; Haoyu Dong; Ping Luo; Shi Han; Dongmei Zhang; | arxiv-cs.CL | 2024-05-13 |
| 943 | Faithful Temporal Question Answering Over Heterogeneous Sources Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As implicit questions are sparse in prior benchmarks, we introduce a principled method for generating diverse questions. |
Zhen Jia; Philipp Christmann; Gerhard Weikum; | www | 2024-05-13 |
| 944 | MedConceptsQA: Open Source Medical Concepts QA Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present MedConceptsQA, a dedicated open source benchmark for medical concepts question answering. |
Ofir Ben Shoham; Nadav Rappoport; | arxiv-cs.CL | 2024-05-12 |
| 945 | SalChartQA: Question-driven Saliency on Information Visualisations Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Understanding the link between visual attention and users’ information needs when visually exploring information visualisations is under-explored due to a lack of large and … |
YAO WANG et. al. | Proceedings of the CHI Conference on Human Factors in … | 2024-05-11 |
| 946 | Prompting Large Language Models with Knowledge Graphs for Question Answering Involving Long-tail Facts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Since LLMs have probably seen the majority of factual question-answering datasets already, to facilitate our analysis, we proposed a fully automatic pipeline for creating a benchmark that requires knowledge of long-tail facts for answering the involved questions. |
WENYU HUANG et. al. | arxiv-cs.CL | 2024-05-10 |
| 947 | Multimodal Attention-driven Visual Question Answering for Malayalam Related Papers Related Patents Related Grants Related Venues Related Experts View |
Abhishek Gopinath Kovath; Anand Nayyar; O. K. Sikha; | Neural Comput. Appl. | 2024-05-10 |
| 948 | AgriPrompt: A Method to Enhance ChatGPT for Agricultural Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: We propose a method called AgriPrompt to enhance the agricultural question-answering capability of ChatGPT. We propose a BERT-based model, AgriParse, to extract semantic … |
TIANYUE CHEN et. al. | 2024 27th International Conference on Computer Supported … | 2024-05-08 |
| 949 | S-EQA: Tackling Situational Queries in Embodied Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: We present and tackle the problem of Embodied Question Answering (EQA) with Situational Queries (S-EQA) in a household environment. Unlike prior EQA work tackling simple queries … |
VISHNU SASHANK DORBALA et. al. | ArXiv | 2024-05-08 |
| 950 | Mitigating Clickbait: An Approach to Spoiler Generation Using Multitask Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study introduces ‘clickbait spoiling’, a novel technique designed to detect, categorize, and generate spoilers as succinct text responses, countering the curiosity induced by clickbait content. |
Sayantan Pal; Souvik Das; Rohini K. Srihari; | arxiv-cs.CL | 2024-05-07 |
| 951 | VSA4VQA: Scaling A Vector Symbolic Architecture to Visual Question Answering on Natural Images Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose VSA4VQA – a novel 4D implementation of VSAs that implements a mental representation of natural images for the challenging task of Visual Question Answering (VQA). |
Anna Penzkofer; Lei Shi; Andreas Bulling; | arxiv-cs.CV | 2024-05-06 |
| 952 | Overview of The EHRSQL 2024 Shared Task on Reliable Text-to-SQL Modeling on Electronic Health Records Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we describe the task of reliable text-to-SQL modeling, the dataset, and the methods and results of the participants. |
Gyubok Lee; Sunjun Kweon; Seongsu Bae; Edward Choi; | arxiv-cs.CL | 2024-05-04 |
| 953 | UQA: Corpus for Urdu Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces UQA, a novel dataset for question answering and text comprehension in Urdu, a low-resource language with over 70 million native speakers. |
Samee Arif; Sualeha Farid; Awais Athar; Agha Ali Raza; | arxiv-cs.CL | 2024-05-02 |
| 954 | Enhanced Textual Feature Extraction for Visual Question Answering: A Simple Convolutional Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we conduct a comprehensive comparison between complex textual models that leverage long-range dependencies and simpler models focusing on local textual features within a well-established VQA framework. |
Zhilin Zhang; | arxiv-cs.CV | 2024-05-01 |
| 955 | ZVQAF: Zero-shot Visual Question Answering with Feedback from Large Language Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Cheng Liu; Chao Wang; Yan Peng; Zhixu Li; | Neurocomputing | 2024-05-01 |
| 956 | Video Question Answering With Semantic Disentanglement and Reasoning Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Video question answering aims to provide correct answers given complex videos and related questions, posting high requirements of the comprehension ability in both video and … |
Jin Liu; Guoxiang Wang; Jialong Xie; F. Zhou; Huijuan Xu; | IEEE Transactions on Circuits and Systems for Video … | 2024-05-01 |
| 957 | ConfigILM: A General Purpose Configurable Library for Combining Image and Language Models for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
L. Hackel; Kai Norman Clasen; Begum Demir; | SoftwareX | 2024-05-01 |
| 958 | Cascade Transformers with Dynamic Attention for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
Yimin Jiang; Tingfei Yan; Mingze Yao; Huibing Wang; Wenzhe Liu; | Comput. Vis. Image Underst. | 2024-05-01 |
| 959 | Question-Aware Global-Local Video Understanding Network for Audio-Visual Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: As a newly emerging task, audio-visual question answering (AVQA) has attracted research attention. Compared with traditional single-modality (e.g., audio or visual) QA tasks, it … |
Zailong Chen; Lei Wang; Peng Wang; Peng Gao; | IEEE Transactions on Circuits and Systems for Video … | 2024-05-01 |
| 960 | Suvach — Generated Hindi QA Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a new benchmark specifically designed for evaluating Hindi EQA models and discusses the methodology to do the same for any task. |
Vaishak Narayanan; Prabin Raj KP; Saifudheen Nouphal; | arxiv-cs.CL | 2024-04-30 |
| 961 | When to Retrieve: Teaching LLMs to Utilize Information Retrieval Effectively Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we demonstrate how Large Language Models (LLMs) can effectively learn to use an off-the-shelf information retrieval (IR) system specifically when additional context is required to answer a given question. |
Tiziano Labruna; Jon Ander Campos; Gorka Azkune; | arxiv-cs.CL | 2024-04-30 |
| 962 | QLSC: A Query Latent Semantic Calibrator for Robust Extractive Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our work introduces a novel approach, called the “Query Latent Semantic Calibrator (QLSC)”, designed as an auxiliary module for existing MRC models. |
SHENG OUYANG et. al. | arxiv-cs.CL | 2024-04-30 |
| 963 | ViOCRVQA: Novel Benchmark Dataset and Vision Reader for Visual Question Answering By Understanding Vietnamese Text in Images Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Optical Character Recognition – Visual Question Answering (OCR-VQA) is the task of answering text information contained in images that have just been significantly developed in … |
HUY QUANG PHAM et. al. | ArXiv | 2024-04-29 |
| 964 | TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we establish a benchmark for table visual question answering, referred to as the TableVQA-Bench, derived from pre-existing table question-answering (QA) and table structure recognition datasets. |
Yoonsik Kim; Moonbin Yim; Ka Yeon Song; | arxiv-cs.CV | 2024-04-29 |
| 965 | QANA: LLM-based Question Generation and Network Analysis for Zero-shot Key Point Analysis and Beyond Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose Question-Answering Network Analysis (QANA), a novel opinion mining framework that utilizes Large Language Models (LLMs) to generate questions from users’ comments, constructs a bipartite graph based on the comments’ answerability to the questions, and applies centrality measures to examine the importance of opinions. |
TOMOKI FUKUMA et. al. | arxiv-cs.CL | 2024-04-28 |
| 966 | MediFact at MEDIQA-M3G 2024: Medical Question Answering in Dermatology with Multimodal Learning Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: The MEDIQA-M3G 2024 challenge necessitates novel solutions for Multilingual & Multimodal Medical Answer Generation in dermatology (wai Yim et al., 2024a). This paper addresses the … |
Nadia Saeed; | Clinical Natural Language Processing Workshop | 2024-04-27 |
| 967 | From Multiple-Choice to Extractive QA: A Case Study for English and Arabic Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We aim to help others adapt our approach for the remaining 120 BELEBELE language variants, many of which are deemed under-resourced. |
TERESA LYNN et. al. | arxiv-cs.CL | 2024-04-26 |
| 968 | Large Language Models in The Clinic: A Comprehensive Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To better understand LLMs in the clinic, we construct a benchmark ClinicBench. |
FENGLIN LIU et. al. | arxiv-cs.CL | 2024-04-25 |
| 969 | Türkçe Dil Modellerinin Performans Karşılaştırması Performance Comparison of Turkish Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Yet, despite the increasing number of these models, there is no comprehensive comparison of their performance for Turkish. This study aims to fill this gap in the literature. |
EREN DOGAN et. al. | arxiv-cs.CL | 2024-04-25 |
| 970 | Fusion of Domain-Adapted Vision and Language Models for Medical Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a medical vision-language model that integrates large vision and language models adapted for the medical domain. |
CUONG NHAT HA et. al. | arxiv-cs.CL | 2024-04-24 |
| 971 | KS-LLM: Knowledge Selection of Large Language Models with Evidence Document for Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Large language models (LLMs) suffer from the hallucination problem and face significant challenges when applied to knowledge-intensive tasks. A promising approach is to leverage … |
XINXIN ZHENG et. al. | ArXiv | 2024-04-24 |
| 972 | Assessing The Potential Of Mid-Sized Language Models For Clinical QA Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Large language models, such as GPT-4 and Med-PaLM, have shown impressive performance on clinical tasks; however, they require access to compute, are closed-source, and cannot be … |
ELLIOT BOLTON et. al. | ArXiv | 2024-04-24 |
| 973 | Evaluating Tool-Augmented Agents in Remote Sensing Platforms Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Tool-augmented Large Language Models (LLMs) have shown impressive capabilities in remote sensing (RS) applications. However, existing benchmarks assume question-answering input … |
Simranjit Singh; Michael Fore; Dimitrios Stamoulis; | ArXiv | 2024-04-23 |
| 974 | Wiki-LLaVA: Hierarchical Retrieval-Augmented Generation for Multimodal LLMs IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Multimodal LLMs are the natural evolution of LLMs, and enlarge their capabilities so as to work beyond the pure textual modality. As research is being carried out to design novel architectures and vision-and-language adapters, in this paper we concentrate on endowing such models with the capability of answering questions that require external knowledge. |
DAVIDE CAFFAGNI et. al. | arxiv-cs.CV | 2024-04-23 |
| 975 | Sign Language Translation with Hierarchical Memorized Context in Question Answering Scenarios Related Papers Related Patents Related Grants Related Venues Related Experts View |
LIQING GAO et. al. | Neural Comput. Appl. | 2024-04-23 |
| 976 | Retrieval Augmented Generation for Domain-specific Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel framework to compile a large question-answer database and develop the approach for retrieval-aware finetuning of a Large Language model. |
SANAT SHARMA et. al. | arxiv-cs.CL | 2024-04-23 |
| 977 | RS-LLaVA: A Large Vision-Language Model for Joint Captioning and Question Answering in Remote Sensing Imagery IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In this paper, we delve into the innovative application of large language models (LLMs) and their extension, large vision-language models (LVLMs), in the field of remote sensing … |
Y. Bazi; Laila Bashmal; Mohamad Mahmoud Al Rahhal; Riccardo Ricci; F. Melgani; | Remote. Sens. | 2024-04-23 |
| 978 | Generate-on-Graph: Treat LLM As Both Agent and KG in Incomplete Knowledge Graph Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To handle IKGQA, we propose a training-free method called Generate-on-Graph (GoG), which can generate new factual triples while exploring KGs. |
YAO XU et. al. | arxiv-cs.CL | 2024-04-23 |
| 979 | Tree of Reviews: A Tree-based Dynamic Iterative Retrieval Framework for Multi-hop Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Multi-hop question answering is a knowledge-intensive complex problem. Large Language Models (LLMs) use their Chain of Thoughts (CoT) capability to reason complex problems step by … |
JIAPENG LI et. al. | ArXiv | 2024-04-22 |
| 980 | Listen Then See: Video Alignment with Speaker Attention Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce a cross-modal alignment and subsequent representation fusion approach that achieves state-of-the-art results (82.06\% accuracy) on the Social IQ 2.0 dataset for SIQA. |
Aviral Agrawal; Carlos Mateo Samudio Lezcano; Iqui Balam Heredia-Marin; Prabhdeep Singh Sethi; | arxiv-cs.CV | 2024-04-21 |
| 981 | Exploring Diverse Methods in Visual Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study explores innovative methods for improving Visual Question Answering (VQA) using Generative Adversarial Networks (GANs), autoencoders, and attention mechanisms. |
PANFENG LI et. al. | arxiv-cs.CV | 2024-04-21 |
| 982 | Predicting Question Quality on StackOverflow with Neural Networks Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The wealth of information available through the Internet and social media is unprecedented. Within computing fields, websites such as Stack Overflow are considered important … |
M. Al-Ramahi; I. Alsmadi; A. Wahbeh; | ArXiv | 2024-04-20 |
| 983 | MahaSQuAD: Bridging Linguistic Divides in Marathi Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce MahaSQuAD, the first-ever full SQuAD dataset for the Indic language Marathi, consisting of 118,516 training, 11,873 validation, and 11,803 test samples. |
Ruturaj Ghatage; Aditya Kulkarni; Rajlaxmi Patil; Sharvi Endait; Raviraj Joshi; | arxiv-cs.CL | 2024-04-20 |
| 984 | PDF-MVQA: A Dataset for Multimodal Information Retrieval in PDF-based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Through this work, we aim to enhance the capabilities of existing vision-and-language models in handling challenges posed by text-dominant documents in VRD-QA. |
Yihao Ding; Kaixuan Ren; Jiabin Huang; Siwen Luo; Soyeon Caren Han; | arxiv-cs.CV | 2024-04-19 |
| 985 | LaPA: Latent Prompt Assist Model For Medical Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose the Latent Prompt Assist model (LaPA) for medical visual question answering. |
Tiancheng Gu; Kaicheng Yang; Dongnan Liu; Weidong Cai; | arxiv-cs.CV | 2024-04-19 |
| 986 | Characterizing LLM Abstention Behavior in Science QA with Context Perturbations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we study the ability of LLMs to abstain from answering context-dependent science questions when provided insufficient or incorrect context. |
Bingbing Wen; Bill Howe; Lucy Lu Wang; | arxiv-cs.CL | 2024-04-18 |
| 987 | MedThink: Explaining Medical Visual Question Answering Via Multimodal Decision-Making Rationale Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the model interpretability and transparency of existing MedVQA solutions are often limited, posing challenges in understanding their decision-making processes. To address this issue, we devise a semi-automated annotation process to streamline data preparation and build new benchmark MedVQA datasets R-RAD, R-SLAKE and R-Path. |
XIAOTANG GAI et. al. | arxiv-cs.CV | 2024-04-18 |
| 988 | Evaluating AI for Law: Bridging The Gap with Open-Source Solutions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study evaluates the performance of general-purpose AI, like ChatGPT, in legal question-answering tasks, highlighting significant risks to legal professionals and clients. |
Rohan Bhambhoria; Samuel Dahan; Jonathan Li; Xiaodan Zhu; | arxiv-cs.AI | 2024-04-18 |
| 989 | Look, Listen, and Answer: Overcoming Biases for Audio-Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Furthermore, current datasets may not provide a precise diagnostic for these methods. To tackle these challenges, firstly, we propose a novel dataset, MUSIC-AVQA-R, crafted in two steps: rephrasing questions within the test split of a public dataset (MUSIC-AVQA) and subsequently introducing distribution shifts to split questions. |
JIE MA et. al. | arxiv-cs.CV | 2024-04-18 |
| 990 | EuSQuAD: Automatically Translated and Aligned SQuAD2.0 for Basque Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This work presents EuSQuAD, the first initiative dedicated to automatically translating and aligning SQuAD2.0 into Basque, resulting in more than 142k QA examples. |
Aitor García-Pablos; Naiara Perez; Montse Cuadros; Jaione Bengoetxea; | arxiv-cs.CL | 2024-04-18 |
| 991 | Consistency Training By Synthetic Question Generation for Conversational Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: By citing a common modeling error prevalent in previous research, we introduce a new baseline model and compare our model’s performance against it, demonstrating an improvement in results, particularly when dealing with questions that include a substantial amount of historical context. |
Hamed Hematian Hemati; Hamid Beigy; | arxiv-cs.CL | 2024-04-17 |
| 992 | Knowledge-Enriched Prompt for Low-Resource Named Entity Recognition Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Named Entity Recognition (NER) in low-resource settings aims to identify and categorize entities in a sentence with limited labeled data. Although prompt-based methods have … |
Wenlong Hou; Weidong Zhao; Xianhui Liu; Wenyan Guo; | ACM Transactions on Asian and Low-Resource Language … | 2024-04-17 |
| 993 | CoTAR: Chain-of-Thought Attribution Reasoning with Multi-level Granularity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce an attribution-oriented Chain-of-Thought reasoning method to enhance the accuracy of attributions. |
Moshe Berchansky; Daniel Fleischer; Moshe Wasserblat; Peter Izsak; | arxiv-cs.CL | 2024-04-16 |
| 994 | IMCN: Improved Modular Co-attention Networks for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
Cheng Liu; Chao Wang; Yan Peng; | Appl. Intell. | 2024-04-16 |
| 995 | Theme-Enhanced Hard Negative Sample Mining for Open-Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an approach for theme-enhanced hard negative sample mining, called THNSM. |
F. Li; Z. Xie; G. Zhou; | icassp | 2024-04-15 |
| 996 | M3TQA: Multi-View, Multi-Hop and Multi-Stage Reasoning for Temporal Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: 3) Fusion strategies (addition or concatenation) of PLMs and KG information ignore feature differences, resulting in suboptimal solutions. To alleviate the above problems, we propose a novel Multi-view, Multi-hop and Multi-stage reasoning paradigm for TKGQA (M3TQA). |
Z. Zha; P. Qi; X. Bao; M. Tian; B. Qin; | icassp | 2024-04-15 |
| 997 | Is Table Retrieval A Solved Problem? Exploring Join-Aware Multi-Table Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: If the join plan is not considered in the retrieval stage, the subsequent steps of reasoning and answering based on those retrieved tables are likely to be incorrect. To address this problem, we introduce a method that uncovers useful join relations for any query and database during table retrieval. |
Peter Baile Chen; Yi Zhang; Dan Roth; | arxiv-cs.IR | 2024-04-15 |
| 998 | Human Guided Cross-Modal Reasoning with Semantic Attention Learning for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We proposed a novel Human-Guided Cross-Modal Reasoning (HGCMR) with semantic attention learning to improve the reasoning ability. |
L. Liao; M. Feng; M. Yang; | icassp | 2024-04-15 |
| 999 | Visually Dehallucinative Instruction Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents a novel and scalable method for generating visually dehallucinative instructions, dubbed CAP2QA, that constrains the scope to only image contents. |
S. Cha; J. Lee; Y. Lee; C. Yang; | icassp | 2024-04-15 |
| 1000 | Retrieval-Generation Synergy Augmented Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: One is to retrieve from an external knowledge base, and the other is to utilize large language models to generate documents. |
Z. Feng; X. Feng; D. Zhao; M. Yang; B. Qin; | icassp | 2024-04-15 |