Question Answering

Also known as QA

Question answering asks how systems can find, interpret, and combine information to answer questions, from short factual queries to multi-hop reasoning over documents, conversations, images, video, and structured data. Common approaches include retrieval-augmented generation, fine-tuning, knowledge graphs, tool-using agents, and symbolic methods; evaluations span reading comprehension, open-domain and long-document QA, conversational QA, and specialized domains. Current work also focuses on multilingual and multimodal settings, personalization, and recognizing when to clarify or abstain rather than guess. A central tension is that better retrieval or reasoning does not always produce more reliable answers: systems must still handle missing or conflicting evidence, avoid hallucinations, and balance accuracy, coverage, latency, and cost.

Latest papers

263 papers28 papers in the last four weeks, down 20% on the four weeks before. 0.2% of all new papers.

TARAZ: Persian Short-Answer Question Benchmark for Cultural Evaluation of Language Models
Feb 26, 2026Reihaneh Iranmanesh, Saeedeh Davoudi, Pasha Abrishamchian +2Cultural Knowledge in Language Models Cross-Cultural Language Model Evaluation
IR Lab, Computer Science Department Georgetown University, Washington D.C.
Towards Faithful Industrial RAG: A Reinforced Co-adaptation Framework for Advertising QA
Feb 26, 2026Wenwei Li, Ming Xu, Tianle Xia +7Question Answering Retrieval-Augmented Generation
Tencent
LiCQA : A Lightweight Complex Question Answering System
Feb 25, 2026Sourav Saha, Dwaipayan Roy, Mandar MitraQuestion Answering Open-Domain QA
Indian Statistical Institute, Kolkata, India · Indian Institute of Science Education and Research, Kolkata, India
Learning to Reason for Multi-Step Retrieval of Personal Context in Personalized Question Answering
Feb 22, 2026Maryam Amirizaniani, Alireza Salemi, Hamed ZamaniQuestion Answering Retrieval-Augmented Generation
University of Washington Seattle, WA, USA · University of Massachusetts Amherst Amherst, MA, USA
RVR: Retrieve-Verify-Retrieve for Comprehensive Question Answering
Feb 20, 2026Deniz Qian, Hung-Ting Chen, Eunsol ChoiQuestion Answering Evidence Retrieval
Courant Institute School of Mathematics, Computing, and Data Science, New York University
ACE-TA: An Agentic Teaching Assistant for Grounded Q&A, Quiz Generation, and Code Tutoring
Feb 20, 2026Himanshu Tripathi, Charlottee Crowell, Kaley Newlin +3Evidence-Grounded QA Intelligent Tutoring Systems
University of Alabama, Tuscaloosa, Alabama, USA · Brown University, Providence, Rhode Island, USA · Meharry Medical College, Nashville, Tennessee, USA +1
Robustness and Reasoning Fidelity of Large Language Models in Long-Context Code Question Answering
Feb 19, 2026Kishan Maharaj, Nandakishore Menon, Ashita Saxena +1Long-Context Language Model Evaluation Question Answering
IBM Research
Evaluating Monolingual and Multilingual Large Language Models for Greek Question Answering: The DemosQA Benchmark
Feb 18, 2026Charalampos Mastrokostas, Nikolaos Giarelis, Nikos KaracapilidisQuestion Answering Multilingual Language Model Evaluation
Industrial Management and Information Systems Lab, MEAD, University of Patras, Rio Patras, Greece
AgriWorld:A World Tools Protocol Framework for Verifiable Agricultural Reasoning with Code-Executing LLM Agents
Feb 17, 2026Zhixing Zhang, Jesen Zhang, Hao Liu +4Tool-Augmented Language Model Agents LLM Agents
Sun Yat-sen University
CGRA-DeBERTa Concept Guided Residual Augmentation Transformer for Theologically Islamic Understanding
Feb 16, 2026Tahir Hussain, Saddam Hussain KhanQuestion Answering Knowledge Augmentation for Language Models
Artificial Intelligence Lab, Department of Computer Systems Engineering, University of Engineering and Applied Sciences · Artificial Intelligence Lab, Department of Computer Systems Engineering, University of Engineering and Applied Sciences (UEAS), Swat, Pakistan · Interdisciplinary Research Center for Smart Mobility and Logistics +1
The Wikidata Query Logs Dataset
Feb 16, 2026Sebastian Walter, Hannah BastKnowledge Graph Question Answering Knowledge Graphs
University of Freiburg, Department of Computer Science, Freiburg im Breisgau, Germany
Agentic Very Long Video Understanding
Jan 26, 2026Aniket Rege, Arka Sadhu, Yuliang Li +5Question Answering Egocentric Video Understanding
Reality Labs Research at Meta · University of Wisconsin-Madison · Work done during internship at Meta
AfriEconQA: A Benchmark for Quantitative and Temporal Reasoning over World Bank Economic Reports
Jan 6, 2026Edward Ajayi, Mustapha Alaba, David Stephen +1Question Answering Document QA
Carnegie Mellon University Africa Kigali, Rwanda
Counting Clues: A Lightweight Probabilistic Baseline Can Match an LLM
Dec 14, 2025Furong Jia, Yuan Pu, Finn Guo +1Language Model Reasoning Evaluation Clinical QA
Duke University
OGD4All: A Framework for Accessible Interaction with Geospatial Open Government Data Based on Large Language Models
Nov 30, 2025Michael Siebenmann, Javier Argota Sánchez-Vaquerizo, Stefan Arisona +3Geospatial QA Question Answering
Professorship of Computational Social Science, ETH Zurich, 8092 Zürich, Switzerland · Esri R&D Center Zurich, 8005 Zürich, Switzerland · Complexity Science Hub, Vienna, 1030, Austria
Advancing the Pareto Frontier of Test-Time Scaling with Multi-Path Plan Aggregation
Oct 13, 2025Siheng Xiong, Ali Payani, Faramarz FekriTest-Time Scaling LLM Reasoning
Georgia Institute of Technology · Cisco Research
HarmReduction: Benchmarking LLMs in Harm Reduction Information Provision to Support People Who Use Drugs
Jul 29, 2025Kaixuan Wang, Chenxin Diao, Jason T. Jacques +2Healthcare Question Answering
University of Oxford, United Kingdom · University of Edinburgh, United Kingdom · University of St Andrews, United Kingdom +1
FailureSensorIQ: A Multi-Choice QA Dataset for Understanding Sensor Relationships and Failure Modes
Jun 3, 2025Christodoulos Constantinides, Dhaval Patel, Shuxin Lin +3Question Answering LLM Reliability
IBM · IBM TJ Watson Research Center
Popular but Wrong: Understanding and Mitigating LLM Overconfidence through Knowledge Popularity
May 23, 2025Shiyu Ni, Keping Bi, Jiafeng Guo +1LLM Uncertainty Estimation LLM Hallucination Mitigation
CAS Key Lab of Network Data Science and Technology, ICT, CAS · State Key Laboratory of AI Safety · University of Chinese Academy of Sciences
Atomic Consistency Preference Optimization for Long-Form Question Answering
May 14, 2025Jingfeng Chen, Raghuveer Thirukovalluru, Junlin Wang +2LLM Hallucination Mitigation LLM Alignment
Kongzi: A Historical Large Language Model with Fact Enhancement
Apr 13, 2025Jiashu Yang, Ningning Wang, Yian Zhao +5Language Modeling RL for Language Model Reasoning
Dalian University of Technology · Peking University · Nanyang Technological University
Towards Reliable Long-Context Reasoning: Detecting Unanswerable Questions via FactGuard
Apr 8, 2025Qian-Wen Zhang, Biao Liu, Fang Li +5LLM Reliability Question Answering
Tencent YouTu Lab, Beijing, China
QDA-SQL: Questions Enhanced Dialogue Augmentation for Multi-Turn Text-to-SQL
Jun 15, 2024Yinggang Sun, Ziming Guo, Haining Yu +5Synthetic Data Generation Question Answering
School of Cyberspace Science · Harbin Institute of Technology · Harbin, China +4

Feb 26, 2026J/K move · Enter open · S save

TARAZ: Persian Short-Answer Question Benchmark for Cultural Evaluation of Language Models

Reihaneh Iranmanesh, Saeedeh Davoudi, Pasha Abrishamchian, Ophir Frieder+1

IR Lab, Computer Science Department Georgetown University, Washington D.C.

Cultural Knowledge in Language Models Cross-Cultural Language Model Evaluation Automated Evaluation Multilingual Language Model Evaluation Question Answering

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This paper presents a comprehensive evaluation framework for assessing the cultural competence of large language models (LLMs) in Persian. Existing Persian cultural benchmarks rely predominantly on multiple-choice formats and English-centric metrics that fail to capture Persian's morphological complexity and semantic nuance. Our framework introduces a Persian-specific short-answer evaluation that combines rule-based morphological normalization with a hybrid syntactic and semantic similarity module, enabling robust soft-match scoring beyond exact string overlap. Through systematic evaluation of 15 state-of-the-art open- and closed-source models across three culturally grounded Persian datasets, we demonstrate that our hybrid evaluation improves scoring consistency by +10 compared to exact-match baselines by capturing meaning that surface-level methods cannot detect. Our human evaluation further confirms that the proposed semantic similarity metric achieves higher agreement with human judgments than LLM-based judges. We publicly release our evaluation framework, providing the first standardized benchmark for measuring cultural understanding in Persian and establishing a reproducible foundation for cross-cultural LLM evaluation research.