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.

Domain Adaptation of Large Language Models for Polymer-Composite Additive Manufacturing Using Retrieval-Augmented Generation and Fine-Tuning
Apr 2, 2026Saiful Islam Sagor, Tania Haghighi, Minhaj Nur Alam +1Domain Adaptation for LLMs LLM Fine-Tuning
Department of Mechanical Engineering and Engineering Science, University of North Carolina at Charlotte, NC 28223, USA. · Department of Electrical and Computer Engineering, University of North Carolina at Charlotte, NC 28223, USA.
Ego-Grounding for Personalized Question-Answering in Egocentric Videos
Apr 2, 2026Junbin Xiao, Shenglang Zhang, Pengxiang Zhu +1Egocentric Video QA Question Answering
University of Science and Technology of China · National University of Singapore
Generative AI-Based Virtual Assistant using Retrieval-Augmented Generation: An evaluation study for bachelor projects
Apr 1, 2026Dumitru Verşebeniuc, Martijn Elands, Sara Falahatkar +4Educational QA Question Answering
Department of Advanced Computing Sciences, Maastricht University, Maastricht 6200 MD, The Netherlands
Long-Document QA with Chain-of-Structured-Thought and Fine-Tuned SLMs
Mar 31, 2026Zhuowen Liang, Xiaotian Lin, Zhengxuan Zhang +3Question Answering Document QA
The Hong Kong University of Science and Technology (Guangzhou) · EvenUp, USA
Retrieval Improvements Do Not Guarantee Better Answers: A Study of RAG for AI Policy QA
Mar 25, 2026Saahil Mathur, Ryan David Rittner, Vedant Ajit Thakur +2Retrieval-Augmented Generation Question Answering
Department of Computer Science, Purdue University, West Lafayette, IN 47907 · Department of Political Science, Purdue University, West Lafayette, IN 47907
CoCR-RAG: Enhancing Retrieval-Augmented Generation in Web Q&A via Concept-oriented Context Reconstruction
Mar 25, 2026Kaize Shi, Xueyao Sun, Qika Lin +4Retrieval-Augmented Generation Question Answering
University of Southern Queensland, QLD, Australia · University of Technology Sydney, NSW, Australia · The Hong Kong Polytechnic University, Hong Kong, China +3
Efficient Fine-Tuning Methods for Portuguese Question Answering: A Comparative Study of PEFT on BERTimbau and Exploratory Evaluation of Generative LLMs
Mar 22, 2026Mariela M. Nina, Caio Veloso Costa, Lilian Berton +1Parameter-Efficient Fine-Tuning Question Answering
Institute of Science and Technology, Federal University of São Paulo (UNIFESP), São José dos Campos, SP, Brazil
KLDrive: Fine-Grained 3D Scene Reasoning for Autonomous Driving based on Knowledge Graph
Mar 22, 2026Ye Tian, Jingyi Zhang, Zihao Wang +4Question Answering LLM Reasoning with Graphs
University of California San Diego, La Jolla, California, USA · University of California Merced, Merced, California, USA
RLVR Training of LLMs Does Not Improve Thinking Ability for General QA: Evaluation Method and a Simple Solution
Mar 21, 2026Kaiyuan Li, Jing-Cheng Pang, Yang YuLanguage Model Reasoning Evaluation RL for Language Model Reasoning
National Key Laboratory for Novel Software Technology & School of Artificial Intelligence, Nanjing University, China · Polixir.ai, China · Huawei Technologies Co., Ltd., China
AgriIR: A Scalable Framework for Domain-Specific Knowledge Retrieval
Mar 17, 2026Shuvam Banerji Seal, Aheli Poddar, Alok Mishra +1Retrieval-Augmented Generation Information Retrieval
Indian Institute of Science Education and Research, Kolkata, India · Institute of Engineering & Management, Kolkata, India
CounterRefine: Answer-Conditioned Counterevidence Retrieval for Inference-Time Knowledge Repair in Factual Question Answering
Mar 17, 2026Tianyi Huang, Ying Kai DengFactual QA Question Answering
Ryquo · App-In Club
Indirect Question Answering in English, German and Bavarian: A Challenging Task for High- and Low-Resource Languages Alike
Mar 16, 2026Miriam Winkler, Verena Blaschke, Barbara PlankQuestion Answering Multilingual QA
UMaiNLP lab, Center for Information and Language Processing, LMU Munich, Germany · ÆMunich Center for Machine Learning, Germany
Distilling Reasoning Without Knowledge: A Framework for Reliable LLMs
Mar 15, 2026Auksarapak Kietkajornrit, Jad Tarifi, Nima AsgharbeygiLLM Planning LLM Reliability
Integral AI
Compositional Consistency-Guided Decoding for Three-Way Logical Question Answering
Mar 12, 2026Tianyi Huang, Ming Hou, Jiaheng Su +2Question Answering Logical Reasoning
Ryquo · App-In Club
Hallucination as output-boundary misclassification: a composite abstention architecture for language models
Mar 12, 2026Angelina HintsanenLLM Hallucination Mitigation Hallucination in Language Models
NEXUS Laboratory, Helsinki, Finland
Disentangling Prompt Element Level Risk Factors for Hallucinations and Omissions in Mental Health LLM Responses
Mar 10, 2026Congning Ni, Sarvech Qadir, Bryan Steitz +14Hallucination in Language Models LLM Safety Evaluation
Vanderbilt University Medical Center, Nashville, TN, USA · Vanderbilt University, Nashville, TN, USA · Lirio, Knoxville, TN, USA +1
Fanar-Sadiq: A Multi-Agent Architecture for Grounded Islamic QA
Mar 9, 2026Ummar Abbas, Mourad Ouzzani, Mohamed Y. Eltabakh +7Question Answering LLM Grounding
Qatar Computing Research Institute, HBKU, Qatar
Beyond Static: Related Questions Retrieval Through Conversations in Community Question Answering
Mar 9, 2026Xiao Ao, Jie Zou, Yibiao Wei +2Information Retrieval Question Answering
School of Computer Science and Engineering, University of Electronic Science and Technology of China · School of Management Science and Engineering, Southwestern University of Finance and Economics
AI Meets Mathematics Education: A Case Study on Supporting an Instructor in a Large Mathematics Class with Context-Aware AI
Mar 9, 2026Jérémy Barghorn, Anna Sotnikova, Sacha Friedli +1Mathematics Education Educational QA
École polytechnique fédérale de Lausanne Switzerland
Gradually Excavating External Knowledge for Implicit Complex Question Answering
Mar 9, 2026Chang Liu, Xiaoguang Li, Lifeng Shang +4Iterative RAG Question Answering
The University of Hong Kong · Huawei Noah’s Ark Lab
Benchmarking Large Language Models for Quebec Insurance: From Closed-Book to Retrieval-Augmented Generation
Mar 8, 2026David Beauchemin, Richard KhouryLLM Evaluation LLM Reliability
Group for Research in Artificial Intelligence of Laval University (GRAIL), Université Laval, Quebec City, Quebec, Canada
Deep Research, Shallow Evaluation: A Case Study in Meta-Evaluation for Long-Form QA Benchmarks
Mar 6, 2026Jena D. Hwang, Varsha Kishore, Amanpreet Singh +9Automated Evaluation RAG Evaluation
Allen Institute for AI
The EpisTwin: A Knowledge Graph-Grounded Neuro-Symbolic Architecture for Personal AI
Mar 6, 2026Giovanni Servedio, Potito Aghilar, Alessio Mattiace +6Personal AI Agents Neuro-Symbolic Reasoning
Politecnico di Bari · Universit`a degli Studi della Tuscia
NCTB-QA: A Large-Scale Bangla Educational Question Answering Dataset and Benchmarking Performance
Mar 5, 2026Abrar Eyasir, Tahsin Ahmed, Muhammad IbrahimEducational QA Question Answering
Department of Computer Science and Engineering, University of Dhaka
Nepali Passport Question Answering: A Low-Resource Dataset for Public Service Applications
Mar 4, 2026Funghang Limbu Begha, Praveen Acharya, Bal Krishna BalInformation Retrieval Question Answering
ILPRL, Department of Computer Science & Engineering, Kathmandu University, Dhulikhel, Nepal · School of Computing, Dublin City University, Dublin, Ireland
FINEST: Improving LLM Responses to Sensitive Topics Through Fine-Grained Evaluation
Mar 4, 2026Juhyun Oh, Nayeon Lee, Chani Jung +5Language Model Generation Evaluation Language Model Safety Evaluation
School of Computing, KAIST, Daejeon, Republic of Korea · Samsung, Suwon, Republic of Korea
Suppressing Domain-Specific Hallucination in Construction LLMs: A Knowledge Graph Foundation for GraphRAG and QLoRA on River and Sediment Control Technical Standards
Mar 3, 2026Takato YasunoQuestion Answering Graph Retrieval-Augmented Generation
Generative Active Testing: Efficient LLM Evaluation via Proxy Task Adaptation
Feb 26, 2026Aashish Anantha Ramakrishnan, Ardavan Saeedi, Hamid Reza Hassanzadeh +2LLM Evaluation Uncertainty Sampling
The Pennsylvania State University · Optum AI
SPARTA: Scalable and Principled Benchmark of Tree-Structured Multi-hop QA over Text and Tables
Feb 26, 2026Sungho Park, Jueun Kim, Wook-Shin HanTable QA Question Answering
POSTECH, Pohang, Republic of Korea
MTRAG-UN: A Benchmark for Open Challenges in Multi-Turn RAG Conversations
Feb 26, 2026Sara Rosenthal, Yannis Katsis, Vraj Shah +3Retrieval-Augmented Generation RAG Evaluation
IBM Research, USA

Apr 2, 2026J/K move · Enter open · S save

Domain Adaptation of Large Language Models for Polymer-Composite Additive Manufacturing Using Retrieval-Augmented Generation and Fine-Tuning

Saiful Islam Sagor, Tania Haghighi, Minhaj Nur Alam, Erina Baynojir Joyee

Department of Mechanical Engineering and Engineering Science, University of North Carolina at Charlotte, NC 28223, USA. · Department of Electrical and Computer Engineering, University of North Carolina at Charlotte, NC 28223, USA.

Domain Adaptation for LLMs LLM Fine-Tuning Retrieval-Augmented Generation Question Answering Additive Manufacturing

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General-purpose large language models (LLMs) often struggle to generate reliable responses in specialized engineering domains due to limited domain grounding and insufficient exposure to structured technical knowledge. This study investigates practical strategies for adapting a foundation LLM to the additive manufacturing (AM) domain in order to improve answer accuracy, relevance, and usability for expert-level question answering. AM knowledge is distributed across heterogeneous sources such as academic literature, manufacturer documentation, technical standards, and procedural guides. Although general LLMs demonstrate strong linguistic capabilities, they frequently fail to retrieve and contextualize such domain-specific information. Two common approaches to address this limitation are domain-specific fine-tuning and retrieval-augmented generation (RAG). We construct a curated AM corpus and evaluate three configurations based on LLaMA-3-8B: (1) the pretrained baseline model, (2) a RAG system that retrieves relevant document chunks from a vector database, and (3) a model fine-tuned on raw domain text. Performance is evaluated using 200 expert-designed AM questions assessed by mechanical engineering experts for accuracy, relevance, and overall preference. Results show that the RAG model consistently outperforms the baseline. Among the 200 questions, 75.5% of RAG responses are judged more accurate, 85.2% are preferred overall, and 90.8% are rated more relevant than baseline responses. In contrast, fine-tuning on raw AM text reduces performance, producing more accurate answers in only 5.6% of cases and more relevant answers in 32.5% of cases. These results indicate that retrieval-augmented approaches provide a more effective pathway for adapting LLMs to specialized engineering domains than naive fine-tuning on unstructured technical data.