LLM Hallucination Detection

LLM: Large Language Model

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13 papers in the last four weeks, up 63% on the four weeks before. 0.1% of all new papers.

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CARE: A Conformal Safety Layer for Medical Summarization
Jun 8, 2026Suhana Bedi, Bridget Lin, Anson Y. Zhou +5Text Summarization Conformal Risk Control
Department of Biomedical Data Science, Stanford University · Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University · Department of Computer Science, Stanford University +2
Constrained Paraphrase Consistency for LLM Hallucination Detection
Jun 6, 2026Shanshan Lin, Dongsheng Hong, Sibo Ju +3LLM Reliability LLM Hallucination Detection
1Fuzhou University · 2Harbin Institute of Technology (Shenzhen) · 3Beijing University of Posts and Telecommunications
Cross Paraphrastic Invariance Learning for Hallucination Detection
Jun 6, 2026Shanshan Lin, Dongsheng Hong, Sibo Ju +3Contrastive Learning LLM Grounding
1Fuzhou University · 2Harbin Institute of Technology (Shenzhen) · 3The Hong Kong University of Science and Technology (Guangzhou)
OpenHalDet: A Unified Benchmark for Hallucination Detection across Diverse Generation Scenarios
Jun 5, 2026Xinyi Li, Zhen Fang, Yongxin Deng +12LLM Evaluation Hallucination Detection
University of Technology Sydney · University of Bristol · University of Wisconsin–Madison +2
Evidence Graph Consistency in Retrieval-Augmented Generation: A Model-Dependent Analysis of Hallucination Detection
Jun 4, 2026Jianru ShenRetrieval-Augmented Generation Hallucination in Language Models
University of Montana Missoula, MT, USA
Cascading Hallucination in Agentic RAG: The CHARM Framework for Detection and Mitigation
Jun 3, 2026Saroj MishraLLM Hallucination Mitigation Hallucination in Language Models
University of North Dakota
DECK: A Consistency x Confidence Taxonomy of LLM Hallucinations
Jun 1, 2026Mohit Singh ChauhanLanguage Model Error Detection LLM Hallucination Detection
Med-HEAL: Analyzing and Mitigating Hallucinations in Medical LLMs with Hallucination-Aware In-Context Learning
May 31, 2026Yiming Liao, Zeno Franco, Jose Eduardo Lizarraga Mazaba +1LLM Self-Correction LLM Hallucination Mitigation
University of Maryland, Baltimore County Baltimore, Maryland, USA · Concordia University Wisconsin · Medical College of Wisconsin Milwaukee, Wisconsin USA
TriLens: Per-Layer Logit-Lens Entropy for White-Box Hallucination Detection
May 31, 2026Bohan Yang, Yijun Gong, Zhi Zhang +3Language Model Error Detection LLM Reliability
Binjiang Institute of Zhejiang University · Beijing Normal-Hong Kong Baptist University · Great Bay University +1
Citation Grounding: Detecting and Reducing LLM Citation Hallucinations via Legal Citation Graphs
May 30, 2026Volodymyr OvcharovLegal Citation Verification LLM Hallucination Mitigation
LEX AI LLC, Kyiv, Ukraine
Hallucination Is Linearly Decodable from Mid-Layer Hidden States in Quantized LLMs
May 30, 2026Aizierjiang AiersilanHallucination in Language Models LLM Hallucination Detection
University of Macau
KG-Guard: Graph-Based Hallucination Detection for Knowledge Base Question Answering
May 29, 2026Albert Sawczyn, Piotr Bielak, Tomasz KajdanowiczLLM Hallucination Detection Knowledge Graph Question Answering
Department of Artificial Intelligence Wrocław University of Science and Technology Wrocław, Poland
FLaG: Fine-Grained Latent Grouping for Hallucination Detection
May 29, 2026Wentao Ye, Liyao Li, Zhiqing Xiao +6Latent Variable Models LLM Hallucination Detection
Zhejiang University · Hangzhou, Zhejiang, China · Nanyang Technological University +1
Protocol for evaluating ChatGPT in biomedical association generation and verification using a RAG-enabled, cross-model majority voting workflow
May 28, 2026Ahmed Abdeen Hamed, Luis M. RochaLLM Evaluation LLM Answer Verification
Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE 68588, USA · School of Systems Science & Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA · Universidade Catόlica Portuguesa, Catόlica Biomedical Research Centre, Lisbon, Portugal
K-FinHallu: A Hallucination Detection Benchmark for Multi-Turn RAG in Korean Finance
May 28, 2026Eunbyeol Cho, Yunseung Lee, Mirae Kim +3Retrieval-Augmented Generation Hallucination in Language Models
KAIST AI · Financial Tech Lab, KakaoBank Corp.
Entropy Distribution as a Fingerprint for Hallucinations in Generative Models
May 27, 2026Mattia J. Villani, Pranav Deshpande, Akshay Seshadri +2Language Model Error Detection Language Model Generation Evaluation
Global Technology Applied Research, JPMorganChase, New York, NY 10001, USA
Chain-based Adaptive Reconfiguration Over Lattices for Hallucination Reduction
May 26, 2026Joan Vendrell Gallart, Solmaz Kia, Russell Bent +1LLM Hallucination Mitigation Hallucination in Language Models
Department of Mechanical and Aerospace University of California Irvine Irvine, CA 92617-4322, USA · T-5 Los Alamos National Laboratory Los Alamos, NM 88220, USA · CAI-4 Los Alamos National Laboratory Los Alamos, NM 88220, USA
CiteCheck: Retrieval-Grounded Detection of LLM Citation Hallucinations in Scientific Text
May 26, 2026Khashayar Khajavi, Shaghayegh Sadeghi, Rise Adhikari +1Citation Verification Automated Fact-Checking
FirstPrinciples · School of Computing Science, Simon Fraser University
QUACK: Questioning, Understanding, and Auditing Communicated Knowledge in Multimodal Social Deduction Agents
May 26, 2026Ye Yuan, Rui Song, Weien Li +12Social Deduction Games LLM Agent Evaluation
McGill University · Mila - Quebec AI Institute · University of Cambridge +3
Evaluating the Relevance of Uncertainty Estimators for LLM Hallucination
May 26, 2026Yedidia Agnimo, Anna Korba, Annabelle Blangero +2LLM Evaluation Hallucination in Language Models
Ekimetrics · Centre Inria de l’Université Grenoble Alpes · CREST, ENSAE +1
Neuro-Symbolic Verification of LLM Outputs for Data-Sensitive Domains (extended preprint)
May 26, 2026Paul Sigloch, Christoph BenzmüllerLLM Answer Verification LLM Hallucination Detection
University of Bamberg, Bamberg, Germany · Free University of Berlin, Berlin, Germany
Automatic Layer Selection for Hallucination Detection
May 25, 2026Xinpeng Wang, William X. Cao, Andrew Gordon Wilson +1Hallucination in Language Models LLM Hallucination Detection
University of Virginia · 2Walrus Security · 3New York University.
Why LLMs Hallucinate on Structured Knowledge: A Mechanistic Analysis of Reasoning over Linearized Representations
May 25, 2026Shanghao Li, Jinda Han, Yibo Wang +5LLM Grounding LLM Interpretability
University of Illinois Chicago · University of Illinois Urbana-Champaign
MultiHaluDet: Multilingual Hallucination Detection via LLM Hidden State Probing
May 24, 2026Riasad Alvi, Nurul Labib Sayeedi, Md. Faiyaz Abdullah SayeediHallucination Detection Hallucination in Language Models
United International University · BRAC University
Beyond Final Answers: Auditing Trajectory-Level Hallucinations in Multi-Agent Industrial Workflows
May 22, 2026Harshada Badave, Santosh Borse, Andrea Gomez +6AI Agent Reliability AI Agent Auditing
1IBM · 2Columbia University
Empirical Analysis and Detection of Hallucinations in LLM-Generated Bug Report Summaries
May 22, 2026Hinduja Nirujan, Shreyas Patil, Abdallah Ayoub +2Software Engineering Text Summarization
Electrical and Software Engineering University of Calgary Calgary, Alberta, Canada
Graph Alignment Topology as an Inductive Bias for Grounding Detection
May 21, 2026Paul Landes, Pranav Herur, Adam Cross +1LLM Grounding Hallucination in Language Models
†Department of Pediatrics, University of Illinois College of Medicine Peoria · ♣University of Illinois Urbana-Champaign · ♠Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign +1
Do No Harm? Hallucination and Actor-Level Abuse in Web-Deployed Medical Large Language Models
May 20, 2026Sunday Oyinlola Ogundoyin, Muhammad Ikram, Rahat MasoodLanguage Model Safety Evaluation Healthcare
Macquarie University, Sydney, Australia · The University of New South Wales, Sydney, Australia
HalluWorld: A Controlled Benchmark for Hallucination via Reference World Models
May 19, 2026Emmy Liu, Varun Gangal, Michael Yu +4LLM Evaluation Hallucination in Language Models
Carnegie Mellon University · All authors are affiliated with DegenAI Labs. · Patronus AI +3
Position: Uncertainty Quantification in LLMs is Just Unsupervised Clustering
May 19, 2026Tiejin Chen, Longchao Da, Xiaoou Liu +1LLM Evaluation Uncertainty Quantification
School of Computing and Augmented Intelligence, Arizona State University

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CARE: A Conformal Safety Layer for Medical Summarization

Suhana Bedi, Bridget Lin, Anson Y. Zhou, Chloe O. Stanwyck+4

Department of Biomedical Data Science, Stanford University · Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University · Department of Computer Science, Stanford University+2

Text Summarization Conformal Risk Control LLM Hallucination Detection Medical LLMs

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Large language models (LLMs) are increasingly used for medical summarization, but their outputs can omit medically important information and introduce unsupported claims. Existing error-detection methods produce heuristic or uncalibrated scores, providing no formal control over missed errors and no principled way to trade off safety against clinician review burden. We introduce Conformal Assessment for Risk Evaluation (CARE), a post-hoc, model-agnostic safety layer that uses conformal risk control to overlay calibrated omission and hallucination flags onto summaries from any LLM without retraining. CARE provides finite-sample, distribution-free guarantees through two controllers: a hallucination controller that bounds the probability of a document containing any unflagged hallucinated sentence, and an omission controller that bounds the expected fraction of important omissions not surfaced for review. Unlike hallucination detection, omissions depend jointly on whether a source sentence is important and whether it is covered by the summary. We show that calibrating only one dimension can violate the target risk bound, while marginal decompositions remain valid but overly conservative. By jointly calibrating over the full

(τ,γ)

threshold space, CARE preserves formal guarantees while surfacing up to 5

\times

fewer sentences than alternative calibrated baselines. Across five medical summarization tasks, CARE satisfies the target risk bound at

α= 0.15

with 95% confidence across 100 calibration/test resplits, using only ~100 labeled documents per domain. In a preliminary clinician study (75 document reviews), calibrated flags improved omission detection by 28.6 percentage points on average. These results show that sentence-level safety guarantees are feasible for LLM-assisted medical summarization and offer a tunable mechanism for balancing residual risk and review effort.

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