Factual QA

QA: Question Answering

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25 papers2 papers in the last four weeks, down 83% on the four weeks before. 0.0% of all new papers.

ListQA: A Benchmark for Evaluating List-Formatted Factual Knowledge Retrieval in Large Language Models
Oct 13, 2026Mayank Kulkarni, Rakesh VaideeswaranFactual QA Factual Knowledge in Language Models
Amazon AGI
Strong yet Controllable LLM Abstention without Learning to Abstain
Oct 12, 2026Junwei Zhang, Yifei Wang, Wanli Yang +6Factual QA LLM Abstention
State Key Laboratory of AI Safety, Institute of Computing Technology, CAS · University of Chinese Academy of Sciences · Beijing Institute of Technology
Learning a Fact Is Not Learning How to Retrieve It
Oct 2, 2026Chaemin Jang, Jihee Kim, Dongman LeeKnowledge Retrieval in Language Models Factual Knowledge in Language Models
Korea Advanced Institute of Science and Technology (KAIST)
Distilling Directional Verification
Oct 1, 2026Jungseob Lee, Sugyeong Eo, Seongtae Hong +4Factual QA Question Answering
Korea University · Yonsei University Mirae Campus · Soongsil University +1
The Geometry of Contextual Relations: Language Models Address Facts by Order of Mention
Oct 1, 2026Yufa ZhouIn-Context Retrieval Factual Knowledge in Language Models
Duke University
Learning to Retrieve Missing Evidence for Long-Term Memory QA
Sep 29, 2026Yi-Xuan Deng, Yi Zhang, Wei Liu +2RL for IR Evidence Retrieval
Tsinghua University
Knowing Is Not Choosing: What Explicit Verification Adds Beyond Generative Preference
Sep 27, 2026Yilong Li, Chengpo Yan, Aayan Arish +1LLM Evaluation LLM Answer Verification
Department of Computer Science University of Wisconsin-Madison Madison, WI 53703, USA
Contract Memory Compiler: Resolve, Then Traverse
Sep 26, 2026Zhi Song, XiMing Xing, Chunhan Li +9Multi-Hop QA Persistent Memory for Language Models
Department of Computer Science, City University of Hong Kong, Hong Kong SAR · Tencent, China · Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, USA
The Detectability Gap: Hidden Heterogeneity in Hallucination Detection Across Language Models
Sep 25, 2026Pranav Darshan, Pranav A, Sravan Karthick T +2Factual QA Hallucination Detection
Department of Computer Science and Engineering, R.V. College of Engineering, India · CAIRO, Technical University of Applied Sciences Würzburg-Schweinfurt, Germany
Evaluating Sycophancy in Chinese Large Language Models on Factual Questions Derived from Online Search Queries
Sep 25, 2026Geng Liu, Feng Li, Mengxiao Zhu +1Factual QA LLM Sycophancy
Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy · University of Science and Technology of China, Hefei, China
VākQA: A Benchmark and Evaluation Study for Telugu Spoken Factoid Question Answering
Sep 17, 2026Bhavana Akkiraju, Ravi Sastry Kolluru, Sri Charan D +3Audio QA LLM-as-a-Judge
International Institute of Information Technology Hyderabad, India
Diagnosing the Fact-Grounding Gap in Multi-Hop Question Answering
Sep 15, 2026Kevin Mo, Nathan Mo, Richard ZhuMulti-Hop QA Multi-Hop Reasoning
Independent · Northwestern University
NeuroActiSep: Detecting Factual Hallucinations from Feed-Forward Neurons in a Single Pass
Sep 13, 2026Ali Derogar Odolou, Reza Nazari, Mostafa SalehiFactual QA LLM Hallucination Detection
School of Intelligent Systems Engineering College of Interdisciplinary Science and Technology University of Tehran, Tehran, Iran
HyperProve: Answer-Guided Hypergraph Expansion for Multi-Hop Question Answering
Sep 12, 2026An Nguyen Phu, Dung Nguyen Quang, Luu Hieu An +3Multi-Hop QA Factual QA
Hanoi University of Science and Technology · Monash University · University of Oregon
Ingest-Time Fact Compilation for Cost-Efficient and Reliable Question Answering over Revised Corpora
Aug 29, 2026Kyle Wild, Yusuke Takahashi, Asako UrakiFactual QA Temporal QA
Endgame Labs, Inc. San Francisco, USA · Asia AI Institute, Faculty of Data Science Musashino University Tokyo, Japan
PROOF: Profiling Reliability of Object-Level Facts in Large Language Models
Aug 24, 2026Andrei Chetvergov, Mikhail Solovev, Timofei Sivoraksha +4Factual Knowledge in Language Models LLM Evaluation
LitTraceQA: A Benchmark for Multi-Stage Grounding and Verification in Scientific Question Answering
Aug 7, 2026Xuye Liu, Yimu Wang, Peng Shi +7Evidence-Grounded Generation Scientific QA
University of Waterloo · 2Amazon · 3City University of Hong Kong +1
Don't `Well, Actually' Me Unless You Know What You're Talking About: Weak Presupposition Verification Degrades General QA Performance
Aug 6, 2026Shenran Wang, Vered Shwartz, Hila GonenLLM Evaluation LLM Answer Verification
Department of Computer Science, University of British Columbia · Vector Institute · CIFAR AI Chair +1
ResearchQA: Benchmarking Citation-Grounded Question-Answering on Scientific Papers
Jul 13, 2026Saba Imran, Debanjum Singh SolankyLLM Evaluation Citation Verification
Khoj Inc.
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization
Jul 7, 2026Kaishen Wang, Tong Zheng, Xuehao Cui +3Factual QA LLM Hallucination Mitigation
Department of Computer Science, University of Maryland, College Park
Verifiable Rewards Beyond Math and Code: Lightweight Corpus-Grounded Process Supervision for Factual Question Answering
May 28, 2026Shicheng Fan, Haochang Hao, Dehai Min +3Process Reward Models RL for Language Model Reasoning
University of Illinois Chicago
ACL-Verbatim: hallucination-free question answering for research
May 20, 2026Gábor Recski, Szilveszter Tóth, Nadia Verdha +2Hallucination in Language Models Retrieval-Augmented Generation
TU Wien · KR Labs
The First Token Knows: Single-Decode Confidence for Hallucination Detection
May 6, 2026Mina GabrielFactual QA Uncertainty Quantification
Department of Computer and Information Sciences Temple University Philadelphia, PA 19122, USA
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
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

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ListQA: A Benchmark for Evaluating List-Formatted Factual Knowledge Retrieval in Large Language Models

Mayank Kulkarni, Rakesh Vaideeswaran

Amazon AGI

Factual QA Factual Knowledge in Language Models LLM Evaluation Question Answering

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Many real-world questions demand not a single fact but an organized collection of them, yet existing factual knowledge benchmarks almost exclusively target single answer retrieval. We introduce ListQA, a benchmark of 9,045 human-curated, cross-validated questions (audited error rate of 1.7%) that require LLMs to recall multiple facts and compose them into structured lists of 3-10 elements. Questions range from flat lists ("Which NCAA teams went undefeated between 2000 and 2024?") to hierarchical lists with sub-attributes ("...and what was their record and result?"), spanning eight categories across 60+ countries. An evergreen design (explicit temporal constraints or historically immutable facts) keeps ground truth valid without periodic updates. For evaluation, we frame element matching as a linear sum assignment problem: optimal bipartite matching paired with LLM-based semantic grading jointly captures factual accuracy, hallucination, and format compliance. We benchmark 34 models (8 families, 42 configurations across standard and thinking enabled inference) and find: the best standard-inference model scores below 37% LLM-Judge-F1, multi-faceted questions are harder across the board, and providing the source Wikipedia page lifts scores dramatically, confirming that models can organize facts but struggle to retrieve them from parameters alone. Extended analyses cover base vs. instruction-tuned checkpoints and token efficiency. We release the dataset and evaluation code.