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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PARALLAX: Separating Genuine Hallucination Detection from Benchmark Construction Artifacts
May 16, 2026Khizar Hussain, Murat KantarciogluLLM Evaluation Language Model Error Detection
Virginia Tech
HalluScore: Large Language Model Hallucination Question Answering Benchmark
May 16, 2026Aisha Alansari, Hamzah LuqmanMultilingual Language Model Evaluation LLM Evaluation
Department of Information and Computer Science, King Fahd University of Petroleum and Minerals · SDAIA-KFUPM Joint Research Center for Artificial Intelligence
Uncertainty Quantification for Large Language Diffusion Models
May 14, 2026Artem Vazhentsev, Vladislav Smirnov, David Li +3Uncertainty Quantification LLM Hallucination Detection
1MBZUAI · 2The University of Melbourne
When Answers Stray from Questions: Hallucination Detection via Question-Answer Orthogonal Decomposition
May 14, 2026Siyang Yao, Erhu Feng, Yubin XiaHallucination in Language Models LLM Hallucination Detection
Shanghai Jiao Tong University Shanghai,China
Where Does Reasoning Break? Step-Level Hallucination Detection via Hidden-State Transport Geometry
May 13, 2026Tyler Alvarez, Ali BaheriLanguage Model Error Detection Hallucination Detection
Rochester Institute of Technology, Rochester, NY, USA.
CAAFC: Chronological Actionable Automated Fact-Checker for misinformation / non-factual hallucination detection and correction
May 12, 2026Islam Eldifrawi, Shengrui Wang, Amine TrabelsiLLM Hallucination Mitigation Automated Fact-Checking
Scalable Token-Level Hallucination Detection in Large Language Models
May 12, 2026Rui Min, Tianyu Pang, Chao Du +2LLM Evaluation Hallucination in Language Models
Sea AI Lab · Hong Kong University of Science and Technology · 1Sea AI Lab 2Hong Kong University of Science and Technology +2
Instruction Lens Score: Your Instruction Contributes a Powerful Object Hallucination Detector for Multimodal Large Language Models
May 12, 2026Runhe Lai, Xinhua Lu, Yanqi Wu +3Hallucination Detection in VLMs Hallucination Detection
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China · Peng Cheng Laboratory, Shenzhen, China · Key Laboratory of Machine Intelligence and Advanced Computing, MOE, Guangzhou, China.
OptArgus: A Multi-Agent System to Detect Hallucinations in LLM-based Optimization Modeling
May 12, 2026Zhong Li, Zihan Guo, Xiaohan Lu +5LLM Auditing Hallucination in Language Models
♣Great Bay University · ▲Peking University · ♡Jilin University +4
Rethinking Evaluation for LLM Hallucination Detection: A Desiderata, A New RAG-based Benchmark, New Insights
May 11, 2026Wenbo Chen, Veena Padmanabhan, Tootiya Giyahchi +2Hallucination Detection Hallucination in Language Models
Amazon · Carnegie Mellon University
Max-pooling Network Revisited: Analyzing the Role of Semantic Probability in Multiple Instance Learning for Hallucination Detection
May 9, 2026Shota Fujikawa, Issei SatoHallucination in Language Models LLM Hallucination Detection
Department of Computer Science, The University of Tokyo
Source or It Didn't Happen: A Multi-Agent Framework for Citation Hallucination Detection
May 9, 2026Mingzhe Li, Zhiqiang Lin, Shiqing MaHallucination in Language Models Citation Verification
University of Massachusetts Amherst · The Ohio State University
Do Benchmarks Underestimate LLM Performance? Evaluating Hallucination Detection With LLM-First Human-Adjudicated Assessment
May 8, 2026I. F. Atasoy, B. Mutlu, E. A. Sezer +1LLM Evaluation LLM-Assisted Annotation
Department of Computer Engineering, Hacettepe University, Ankara, 06800, Türkiye · Department of Computer Engineering, Ankara University, Ankara, 06830, Türkiye · Zephlen AI and Information Technologies Inc., Kocaeli, 41400, Türkiye
Sanity Checks for Long-Form Hallucination Detection
May 8, 2026Geigh Zollicoffer, Minh Vu, Hongli Zhan +2LLM Evaluation Hallucination Detection
Los Alamos National Laboratory · The University of Texas at Austin · University of British Columbia
LLM hallucinations in the wild: Large-scale evidence from non-existent citations
May 8, 2026Zhenyue Zhao, Yihe Wang, Toby Stuart +3LLM Auditing LLM Hallucination Detection
Department of Information Science, Cornell University, Ithaca, NY, USA · Department of Sociology, University of California Los Angeles, Los Angeles, CA, USA · Department of Computer Science and Technology, Tsinghua University, Beijing, China +1
Hallucination Detection via Activations of Open-Weight Proxy Analyzers
May 8, 2026Akshita Singh, Prabesh Paudel, Siddhartha RoyHallucination in Language Models LLM Reliability
Khoury College of Computer Sciences Northeastern University
Delulu: A Verified Multi-Lingual Benchmark for Code Hallucination Detection in Fill-in-the-Middle Tasks
May 7, 2026Mahdi Erfanian, Nelson Daniel Troncoso, Aashna Garg +4Language Model Error Detection Language Model Generation Evaluation
University of Illinois Chicago · Microsoft
Hallucination as an Anomaly: Dynamic Intervention via Probabilistic Circuits
May 7, 2026Erik Nielsen, Elia Cunegatti, Marcus Vukojevic +1Probabilistic Circuits LLM Hallucination Mitigation
Department of Information Engineering and Computer Science University of Trento Trento, Italy
Attractor Geometry of Transformer Memory: From Conflict Arbitration to Confident Hallucination
May 7, 2026Qiyao Liang, Risto Miikkulainen, Ila FieteMemorization in Language Models Knowledge Conflicts in Language Models
Massachusetts Institute of Technology · University of Texas Austin, Cognizant · Cognizant
The First Token Knows: Single-Decode Confidence for Hallucination Detection
May 6, 2026Mina GabrielUncertainty Quantification Self-Consistency Decoding
Department of Computer and Information Sciences Temple University Philadelphia, PA 19122, USA
Low-Cost Black-Box Detection of LLM Hallucinations via Dynamical System Prediction
May 6, 2026Dan Wilson, Mohamed AkroutHallucination in Language Models LLM Hallucination Detection
University of Tennessee
Text Corpora as Concept Fields: Black-Box Hallucination and Novelty Measurement
May 6, 2026Nicholas S. Kersting, Vittorio Castelli, Chieh Ting Yeh +2LLM Grounding LLM Hallucination Detection
Oracle Corporation
Detecting Hallucinations in Large Language Models via Internal Attention Divergence Signals
May 6, 2026Gijs van DijkLanguage Model Error Detection Confidence Estimation in Language Models
Utrecht University
Self-Attention as Transport: Limits of Symmetric Spectral Diagnostics
May 6, 2026Dominik Dahlem, Diego Maniloff, Mac MisiuraTransformer Interpretability Attention Head Analysis
Red Hat AI
MedFabric and EtHER: A Data-Centric Framework for Word-Level Fabrication Generation and Detection in Medical LLMs
May 5, 2026Tung Sum Thomas Kwok, Qian Qian, Xiaofeng Lin +8Healthcare Hallucination Detection
University of California, Los Angeles CA 90095, USA · Optum AI, USA
Logical Consistency as a Bridge: Improving LLM Hallucination Detection via Label Constraint Modeling between Responses and Self-Judgments
May 5, 2026Hao Mi, Qiang Sheng, Shaofei Wang +7Hallucination in Language Models LLM Hallucination Detection
Institute of Computing Technology, Chinese Academy of Sciences · University of Chinese Academy of Sciences
CuraView: A Multi-Agent Framework for Medical Hallucination Detection with GraphRAG-Enhanced Knowledge Verification
May 5, 2026Severin Ye, Xiao Kong, Xiaopeng He +2Healthcare Hallucination Detection
School of Computer Science and Engineering Kyungpook National University Daegu, Republic of Korea · School of Airspace Science and Engineering Shandong University Weihai, China · Department of English Language and Literature, Kyungpook National University, Daegu, Republic of Korea
A multilingual hallucination benchmark: MultiWikiQHalluA
May 4, 2026Freja Thoresen, Dan Saattrup SmartMultilingual Language Models Multilingual Language Model Evaluation
Alexandra Institute · Rued Langgaards Vej 7, 2300 København
HalluScan: A Systematic Benchmark for Detecting and Mitigating Hallucinations in Instruction-Following LLMs
May 4, 2026Ahmed CherifLLM Evaluation LLM Hallucination Mitigation
1Sofrecom Tunisia, Orange Innovation, Tunis, 1053, Tunisia.
From Flat Facts to Sharp Hallucinations: Detecting Stubborn Errors via Gradient Sensitivity
May 1, 2026Yee Zhing Liew, Andrew Huey Ping Tan, Anwar P. P. Abdul MajeedHallucination Detection Hallucination in Language Models
School of Intelligent Manufacturing Ecosystem, Xi’an Jiaotong-Liverpool University, People’s Republic of China · Department of Computer Science, University of Liverpool, United Kingdom · Faculty of Engineering and Technology, Sunway University, Malaysia +1

May 16, 2026, cs.CLJ/K move · Enter open · S save

PARALLAX: Separating Genuine Hallucination Detection from Benchmark Construction Artifacts

Khizar Hussain, Murat Kantarcioglu

Virginia Tech

LLM Evaluation Language Model Error Detection Hallucination in Language Models LLM Reliability LLM Hallucination Detection Language Model Probing

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Large language models (LLMs) hallucinate with confidence: their outputs can be fluent, authoritative, and simply wrong. In medical, legal, and scientific applications this failure causes direct harm, and detecting it from internal model states offers a path to safer deployment. A growing body of work reports that this problem is increasingly tractable, with recent methods achieving high detection performance on widely used benchmarks. We show, however, that much of this apparent progress does not survive scrutiny. Four of the six corpora embed the ground-truth answer directly in the input prompt. A naïve text-similarity baseline we call \textsc{TxTemb} exploits this to achieve near-perfect detection scores without any access to model internals. To measure what genuine detection capability remains once these artifacts are controlled, we conduct a large-scale evaluation spanning twenty-two detection methods, twelve open-source models spanning six architectural families, and six corpora. We further introduce \textbf{DRIFT}, a supervised probe over inter-layer hidden-state transitions, as a point of comparison for live-generation detection. Our findings suggest that the field's reported progress on hallucination detection is substantially explained by benchmark construction artifacts in widely used corpora, and that the majority of established baselines perform near chance under controlled conditions; the consistent exceptions are SAPLMA and DRIFT, both supervised probes on upper-layer hidden states.

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