Hallucination in Language Models

Follow this topic Ask about this topic

Momentum

13 papers in the last four weeks, up 160% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 187

All topics

Med-StepBench: A Hierarchical Reasoning Framework for Evaluating Hallucinations in Medical Vision-Language Models
May 11, 2026Minh Khoi Nguyen, Dai Lam Le, Amir Reza Jafari +8Healthcare Hallucination Detection
1AI4LIFE, Hanoi University of Science and Technology, Vietnam · 2SAMOVAR, Télécom SudParis, Institut Polytechnique de Paris, France · Military Central Hospital, Vietnam +1
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
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
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
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
CAST: Mitigating Object Hallucination in Large Vision-Language Models via Caption-Guided Visual Attention Steering
May 6, 2026Qiming Li, Zekai Ye, Xiaocheng Feng +9Hallucination in Language Models Large Vision-Language Models
1Harbin Institute of Technology · 2Peng Cheng Laboratory · 3Harbin Institute of Technology, Shenzhen
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
Not All That Is Fluent Is Factual: Investigating Hallucinations of Large Language Models in Academic Writing
May 5, 2026Humam Khan, Md Tabrez Nafis, Shahab Saquib Sohail +2LLM Evaluation LLM Prompting
Department of Computer Science and Engineering, SEST Jamia Hamdard New Delhi, India · Energy, Resources and Industry WSP Calgary, Canada
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
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.
Only Say What You Know: Calibration-Aware Generation for Long-Form Factuality
May 3, 2026Wen Luo, Guangyue Peng, Liang Wang +7Long-Form Document Generation LLM Hallucination Mitigation
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University Research Asia · MSRA · Microsoft
Query-Dependent Use of Generated Descriptions for Reliable Visual Question Answering
May 3, 2026Zeshang Li, Shuoyang ZhangVisual Question Answering Hallucination in Language Models
University of International Relations
Hallucinations Undermine Trust; Metacognition is a Way Forward
May 2, 2026Gal Yona, Mor Geva, Yossi MatiasLLM Hallucination Mitigation Hallucination in Language Models
Google Research · Tel Aviv University
LLM Ghostbusters: Surgical Package Hallucination Suppression via Adaptive Unlearning
May 1, 2026Joseph Spracklen, Pedram Aghazadeh, Farinaz Koushanfar +1LLM Hallucination Mitigation Hallucination in Language Models
University of Texas San Antonio San Antonio, TX, USA · University of California San Diego La Jolla, CA, USA
ReLay: Personalized LLM-Generated Plain-Language Summaries for Better Understanding, but at What Cost?
May 1, 2026Joey Chan, Yikun Han, Jingyuan Chen +8LLM Personalization Language Model Safety Evaluation
University of Illinois Urbana-Champaign · University of Virginia · University of Colorado Anschutz +4
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
Online Self-Calibration Against Hallucination in Vision-Language Models
May 1, 2026Minghui Chen, Chenxu Yang, Hengjie Zhu +3Vision-Language Models Hallucination in Language Models
Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China · School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China
Geometry-Calibrated Conformal Abstention for Language Models
Apr 30, 2026Rui Xu, Yi Chen, Sihong Xie +1Uncertainty Quantification Selective Prediction
Information Hub, AI Thrust Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China
MoRFI: Monotonic Sparse Autoencoder Feature Identification
Apr 29, 2026Dimitris Dimakopoulos, Shay B. Cohen, Ioannis KonstasLLM Hallucination Mitigation LLM Interpretability
University of Edinburgh, UK · Heriot-Watt University, UK
From Architecture to Output: Structural Origins of Hallucination in Large Language Models and the Amplifying Role of Data
Apr 29, 2026Md. Rejaul Korim Sadi, Toufiqur Rahman Tasin, Golam Mostofa NaeemHallucination in Language Models LLM Reliability
*Dept. of Computer Science and Engineering, Metropolitan University, Sylhet, Bangladesh
HIVE: Hidden-Evidence Verification for Hallucination Detection in Diffusion Large Language Models
Apr 28, 2026Guoshenghui Zhao, Tan Yu, Weijie ZhaoHallucination Detection Hallucination in Language Models
Rochester Institute of Technology · NVIDIA Corporation
Prefill-Time Intervention for Mitigating Hallucination in Large Vision-Language Models
Apr 28, 2026Chengsheng Zhang, Chenghao Sun, Xinyan Jiang +2Vision-Language Models Hallucination in Language Models
University of Science and Technology of China · Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, China · University of Chinese Academy of Sciences, Beijing, China
Global Context or Local Detail? Adaptive Visual Grounding for Hallucination Mitigation
Apr 27, 2026Yubo Jiang, Xin Yang, Abudukelimu Wuerkaixi +7Hallucination in Language Models Object Hallucination in VLMs
School of Astronautics, Beihang University, Beijing 102206, China · Longcat Interaction Team, Meituan, Beijing 100102, China · Tianmushan Laboratory, Beihang University, Hangzhou 311115, China

May 11, 2026, cs.CVJ/K move · Enter open · S save

Med-StepBench: A Hierarchical Reasoning Framework for Evaluating Hallucinations in Medical Vision-Language Models

Minh Khoi Nguyen, Dai Lam Le, Amir Reza Jafari, Tuan Dung Nguyen+7

1AI4LIFE, Hanoi University of Science and Technology, Vietnam · 2SAMOVAR, Télécom SudParis, Institut Polytechnique de Paris, France · Military Central Hospital, Vietnam+1

Healthcare Hallucination Detection 3D Medical Imaging Hallucination in Language Models Medical VLMs VLM Reasoning+1

Open paper AskPDF arXiv

Large vision-language models (VLMs) demonstrate strong performance in medical image understanding, but frequently generate clinically plausible yet incorrect statements, raising significant safety concerns. Existing medical hallucination benchmarks primarily focus on 2D imaging with one-shot diagnostic questions, offering limited insight into whether predictions are grounded in correct localization and abnormality identification, allowing critical reasoning errors to remain hidden behind seemingly correct diagnoses. We introduce Med-StepBench, the first large-scale benchmark for step-wise hallucination detection in 3D oncological PET/CT, comprising over 12,000 images and more than 1,000,000 image-statement pairs across volumetric and multi-view 2D data, which decomposes clinical reasoning into four expert-designed diagnostic stages. Using clinician-verified annotations, we perform the first step-level evaluation of general-purpose and medical VLMs, revealing systematic failure modes obscured by aggregate accuracy metrics. Furthermore, we show that current VLMs are highly susceptible to adversarial yet clinically plausible intermediate explanations, which significantly amplify hallucinations despite contradictory visual evidence. Together, our findings highlight fundamental limitations in grounding multi-step clinical reasoning and establish Med-StepBench as a rigorous benchmark for developing safer and more reliable medical VLMs.

Momentum

Related topics

Latest papers 187