Question Answering

Also known as QA

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23 papers in the last four weeks, up 28% on the four weeks before. 0.2% of all new papers.

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A Self Consistency Based Reranking for Narrative Question Answering
Jun 14, 2026Molham Mohamed, Ali HamdiSelf-Consistency Decoding Question Answering
Faculty of Computer Science MSA University Giza, Egypt
Self-Questioning Vision-Language Models: Reinforcement Learning for Compositional Visual Reasoning
Jun 14, 2026Saraswathy AmjithVisual Question Answering Visual Reasoning
Massachusetts Institute of Technology
SANA: What Matters for QA Agents over Massive Data Lakes?
Jun 11, 2026Austin Senna Wijaya, Jiaxiang Liu, Haonan Wang +1LLM Agent Evaluation Question Answering
Columbia University New York, USA
Operads for compositional reasoning in LLMs
Jun 11, 2026Nathaniel Bottman, Kyle RichardsonMulti-Hop QA Compositional Reasoning
Incubilate, Seattle, USA · Allen Institute for Artificial Intelligence, Seattle, USA
A Controlled Study of Decoding-Time Truthfulness Methods on Instruction-Tuned LLMs
Jun 10, 2026Ao SunLLM Evaluation Language Model Decoding
Independent Researcher
uva-irlab-conv at SemEval-2026 Task 8: Multi-Turn RAG with Learned Sparse Retrieval and Listwise Reranking
Jun 10, 2026Simon Lupart, Kidist Amde Mekonnen, Zahra Abbasiantaeb +1Retrieval-Augmented Generation Question Answering
University of Amsterdam Amsterdam, The Netherlands
Crop Recommendation and Agricultural Query Answering System Using Spatio-Temporal Graph Neural Networks and Hybrid Retrieval Augmentation
Jun 8, 2026Prajwal Thapa, Yagya Raj PandeyaQuestion Answering Precision Agriculture
Kathmandu University Dhulikhel, Nepal
When No Answer Is Correct: Diagnosing Absent Answer Detection for MLLMs in Video Understanding
Jun 6, 2026Yiheng Wang, Yueqian Lin, Lichen Zhu +3Multimodal Large Language Models Question Answering
Duke University, Durham, North Carolina, USA
SPARC: A Multi-Agent System for Electrical Circuit Question Answering
Jun 5, 2026Mushtari Sadia, Zhenning Yang, Umme Habiba Lamia +3Multi-Agent LLM Systems Question Answering
University of Michigan, Ann Arbor · Bangladesh University of Engineering and Technology
Improving Answer Extraction in Context-based Question Answering Systems Using LLMs
Jun 4, 2026Hafez Abdelghaffar, Ahmed Alansary, Ali HamdiLLM Fine-Tuning Question Answering
Faculty of Computer Science MSA University Giza, Egypt
IA-RAG: Interval-Algebra-Driven Temporal Reasoning for Dynamic Knowledge Retrieval
Jun 4, 2026Xiaoman Wang, Yaoze Zhang, Wenzhuo Fan +7Retrieval-Augmented Generation Temporal IR
East China Normal University · Shanghai Artificial Intelligence Laboratory · University of Shanghai for Science and Technology +1
Narrative Knowledge Weaver: Narrative-Centric Retrieval-Augmented Reasoning for Long-Form Text Understanding
Jun 4, 2026Qiuyu Tian, Fengyi Chen, Yiding Li +9Retrieval-Augmented Generation Long-Context QA
Southeast University, Nanjing, China · Nanjing Normal University, Nanjing, China · ZhuiWen Technology Co., Ltd., Beijing, China +1
UniCAD: A Unified Benchmark and Universal Model for Multi-Modal Multi-Task CAD
Jun 3, 2026Jingyuan Chen, Sheng Jin, Haopeng Sun +23D Reconstruction Multimodal Large Language Models
SenseTime Research and Tetras.AI
Evaluating LLMs' Effectiveness on Real-World Consumer Device Repair Questions
Jun 2, 2026Atm Mizanur Rahman, Md Arid Hasan, Syed Ishtiaque Ahmed +1Multilingual Language Model Evaluation LLM Evaluation
University of Illinois Urbana-Champaign · University of Toronto
Disagreement-Based Cross-Model Routing for Implicit Video Question Answering
May 31, 2026Durga Sandeep SaluruQuestion Answering Video QA
Independent Researcher
Towards Lightweight Reliability: Using Soft Prompts for Hallucination Mitigation in Large Language Models
May 30, 2026S M Tahmid Siddiqui, Akib Jawad Ononto, Anoop Singhal +1LLM Hallucination Mitigation Question Answering
The University of Texas at Dallas, Richardson, TX 75080, USA · National Institute of Standards and Technology, Gaithersburg, USA
SPADER: Step-wise Peer Advantage with Diversity-Aware Exploration Rewards for Multi-Answer Question Answering
May 30, 2026Qiming Shi, Zhaolu Kang, Yunfan Zhou +2Credit Assignment in RL Step-Level Credit Assignment in RL
State Key Lab of CAD&CG, Zhejiang University · School of Software and Microelectronics, Peking University · School of Software Technology, Zhejiang University
When English Rewrites Local Knowledge: Global Narrative Dominance in Large Language Models
May 28, 2026Md Arid Hasan, Ruwad Naswan, Farhan Samir +2Multilingual Language Model Evaluation LLM Grounding
University of Toronto · BUET · University of Illinois Urbana-Champaign
Can LLM Teams Play What? Where? When?
May 28, 2026Anastasia Kotelnikova, Viktor Byzov, Maria Dolzhenkova +1Multi-Agent LLM Systems LLM Agent Evaluation
Vyatka State University Kirov, Russia · European University at St. Petersburg St. Petersburg, Russia
Selective QA over Conflicting Multi-Source Personal Memory: A Diagnostic Testbed and Method Comparison
May 28, 2026Tiancheng Yang, Matthias Schonlau, Ilia SucholutskyConversational Memory Question Answering
LiveBrowseComp: Are Search Agents Searching, or Just Verifying What They Already Know?
May 27, 2026HuiMing Fan, Xiao Wang, Zheng Chu +5Computer-Use Agent Benchmarks LLM Agent Evaluation
1Harbin Institute of Technology · 2Xiaohongshu
Analyzing Quality-Latency-Resource Trade-offs in a Technical Documentation RAG Assistant Using LoRA Adaptation
May 27, 2026Evgenii Palnikov, Elizaveta GavrilovaLLM Inference Efficiency Retrieval-Augmented Generation
1HSE University
PetroBench: A Benchmark for Large Language Models in Petroleum Engineering
May 27, 2026Xiang Wang, Tingting Zhang, Sen Wang +4LLM Evaluation Question Answering
School of Petroleum and Natural Gas Engineering, Changzhou University, Changzhou 213164, China · China University of Petroleum (East China), Qingdao, Shandong 266580, China
KVoiceBench, KOpenAudioBench, and KMMAU: Agent-Driven Korean Speech Benchmarks for Evaluating SpeechLMs
May 27, 2026Haechan Kim, Seungjun Chung, Inkyu Park +2Audio-Language Model Evaluation Question Answering
1KRAFTON · Department of Mathematical Sciences, Seoul National University
Query Symbolically or Retrieve Semantically? A Dataset and Method for Semi-Structured Question Answering
May 26, 2026Mateusz Czyżnikiewicz, Ryszard Tuora, Adam Kozakiewicz +6Graph Retrieval-Augmented Generation Question Answering
Samsung AI Warsaw · Samsung AI Cambridge
PersLitEval: Fine-grained Benchmark and Evaluation of LLMs on Persian Literature Questions
May 26, 2026Ruhallah Niazi, Faeze Ghorbanpour, Alexander FraserLLM Evaluation Question Answering
School of Computation, Information and Technology, TU Munich · Munich Center for Machine Learning (MCML)
Granuscore: A Reference-Free Measure of Granularity for Text Analysis and Question Answering
May 26, 2026Lukas Ellinger, Alexander Fichtl, Miriam Anschütz +1Question Answering
School for Computation, Information and Technology Technical University of Munich, Germany
Clarify, Abstain or Answer? Strategising in Conversation with Belief-Augmented Generation
May 25, 2026Joris Baan, Wilker Aziz, Barbara Plank +1Clarification Question Generation Question Answering
University of Amsterdam · LMU Munich · European Commission, Joint Research Centre (JRC), Brussels
Inference Time Optimization with Confidence Dynamics
May 24, 2026Yu Wang, Minghao Liu, Jiayun Wang +3Inference-Time Optimization Confidence Estimation in Language Models
Center for Advanced AI, Accenture.
Clarification Is Not Enough: Post-Clarification Answering Remains the Bottleneck in Multi-Turn QA
May 24, 2026Jinyan Su, Jennifer HealeyQuestion Answering Preference Elicitation
1Cornell University · 2Adobe Research

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A Self Consistency Based Reranking for Narrative Question Answering

Molham Mohamed, Ali Hamdi

Faculty of Computer Science MSA University Giza, Egypt

Self-Consistency Decoding Question Answering LLM Reranking

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Narrative question answering (NQA) is a challenging task in natural language processing that requires models to understand long textual contexts, capture relationships across events, and generate coherent responses. Despite recent advances in pretrained language models, most existing approaches rely on a single decoding output during inference, making them sensitive to generation variability and often resulting in incomplete or inconsistent answers .To address this limitation, we propose a self-ensemble Self-Consistency-Based reranking framework for narrative question answering. The proposed method generates multiple candidate answers for each story-question pair and selects the final answer based on semantic agreement among the generated responses. This allows the model to explore diverse answer formulations while improving robustness through consensus-based selection without requiring modifications to the underlying architecture .The framework combines pretrained and fine-tuned language generation with multi-answer inference and similarity-based reranking. We evaluate the proposed approach on the NarrativeQA dataset using multiple models, including FLAN-T5 (Base and Small) and Pegasus-Large, under both baseline and fine-tuned settings .Experimental results demonstrate that the proposed method consistently improves performance across all models. In particular, FLAN-T5-Base achieves the best overall performance, improving from 82.32% to 86.66% (+4.34%) when combined with self-ensemble inference. Additionally, the largest improvement is observed with Pegasus-Large, which increases from 72.50% to 87.07% (+14.57%), highlighting the effectiveness of the proposed strategy.

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Latest papers 165