Audio-Language Model Hallucination Detection

Follow this topic Ask about this topic

Momentum

5 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 14

All topics

MISHAP-Bench: A Hallucination Benchmark for Large Audio-Language Models
Sep 27, 2026Zhi Wen Soi, Giulio Segalini, Jian-Jia Chen +1Audio-Language Model Evaluation Audio-Language Model Hallucination Detection
University of Neuchâtel · TU Dortmund University · RWTH Aachen University +1
REVE: Efficient Hallucination Correction for Large Audio-Language Models via Reused Encoder States
Sep 22, 2026Hongjin Song, Jiasheng Kuang, Xinyu Yang +4Audio-Language Model Hallucination Detection Sound Event Detection
Beijing Institute of Technology, Zhuhai, Guangdong, China
HALDETECT at ImageEval 2026 Shared Tasks: Answer-First Contrastive Grounding with QLoRA
Sep 12, 2026Syed Mohaiminul Hoque, Md Sakhawat HossainVLM Evaluation Hallucination Detection in VLMs
Independent Author
Two-Token Features and Small-Large Ensembles for VLM Hallucination Detection
Sep 9, 2026Eli SchwartzHallucination Detection in VLMs Vision-Language Models
IBM Research
TAD: Token-Adaptive Contrastive Decoding with Confidence-Guided Gating for Hallucination Mitigation in Large Audio-Language Models
Sep 7, 2026Heyu Chang, Nianwen Si, Hao Zhang +2Audio QA Audio-Language Model Hallucination Detection
Information Engineering University, China
UHP Detection: LVLMs have their Unique Hallucination Pattern in the Consistency Space
Aug 4, 2026Amir Mohammad Ezzati, Kiyan Rezaee, Bardiya Kariminia +4Hallucination Detection in VLMs Vision-Language Models
Sharif University of Technology · Michigan State University · Shahid Beheshti University +2
Adaptive Perturbation Selection for Contrastive Audio Decoding
Jun 30, 2026Aaron Isidore Grace, Zhouyuan Huo, Weiran WangAudio-Language Model Evaluation Audio-Language Model Hallucination Detection
Department of Computer Science University of Iowa Iowa City, IA, USA · Google Mountain View, CA, USA
From Text Metrics to Model Internals: A Study of Whisper ASR Hallucination Detection
Jun 22, 2026Jan Jasiński, Mateusz Barański, Julitta Bartolewska +2Audio-Language Model Hallucination Detection Automatic Speech Recognition
Signal Processing Group, Institute of Electronics, AGH University of Krakow, Poland
Hallucination Detection and Correction in Medical VLMs via Counter-Evidence Verification
Jun 17, 2026Nan Zhou, Ke Zou, Meng Liu +5Hallucination Detection in VLMs Vision-Language Models
the College of Computer Science, Sichuan University. · Yong Loo Lin School of Medicine, National University of Singapore. · National Key Laboratory of Autonomous Intelligent Unmanned Systems, Beijing2026 Institute of Technology. +2
Density Ridge Selective Prediction for LLM and VLM Hallucination Detection under Calibration Label Scarcity
Jun 8, 2026Nina I. ShamsiHallucination Detection in VLMs Vision-Language Models
Northeastern University Boston, United States
Whisper Hallucination Detection and Mitigation via Hidden Representation Steering and Sparse AutoEncoders
Jun 5, 2026Georgii Aparin, Vadim Popov, Tasnima Sadekova +1Audio-Language Model Hallucination Detection Sparse Autoencoders
AI Foundation and Algorithm Lab · National Research University Higher School of Economics
HalluCXR: Benchmarking and Mitigating Hallucinations in Medical Vision-Language Models for Chest Radiograph Interpretation
May 19, 2026Haoyu Wang, Zitong LiVision-Language Models Audio-Language Model Hallucination Detection
Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
Detecting Hallucinations in SpeechLLMs at Inference Time Using Attention Maps
Apr 21, 2026Jonas Waldendorf, Bashar Awwad Shiekh Hasan, Evgenii TsymbalovAudio-Language Model Hallucination Detection Hallucination Detection
University of Edinburgh · Amazon AGI
HalluAudio: A Comprehensive Benchmark for Hallucination Detection in Large Audio-Language Models
Apr 21, 2026Feiyu Zhao, Yiming Chen, Wenhuan Lu +3Audio-Language Model Evaluation Audio-Language Model Hallucination Detection
College of Intelligence and Computing, Tianjin University, China · ASUS Intelligent Cloud Services, Singapore

Sep 27, 2026, cs.LGJ/K move · Enter open · S save

MISHAP-Bench: A Hallucination Benchmark for Large Audio-Language Models

Zhi Wen Soi, Giulio Segalini, Jian-Jia Chen, Lydia Chen

University of Neuchâtel · TU Dortmund University · RWTH Aachen University+1

Audio-Language Model Evaluation Audio-Language Model Hallucination Detection Language Model Generation Evaluation Audio-Language Models Hallucination Mitigation

Open paper AskPDF arXiv

Large audio-language models (LALMs) produce fluent responses about audio but often hallucinate by making plausible yet ungrounded claims. Existing audio hallucination benchmarks mainly measure response correctness, leaving it unclear whether an LALM hallucinates or simply fails to understand the audio. We challenge correctness-based evaluation by defining two hallucination categories: (i) context, where claims are not grounded in the audio; and (ii) knowledge, where claims about audio-related topics lack support from externally verifiable facts. We introduce MISHAP-Bench, a comprehensive benchmark with 12,000 challenging open-ended question-audio pairs and a rigorous evaluation pipeline covering both categories. To evaluate open-ended responses, we propose a groundedness judge that uses reference rubrics and judge prompts guided by human annotations. We extensively evaluate ten state-of-the-art LALMs and show that hallucination remains substantial. Even a frontier model such as Gemini 3.7 Flash reaches a hallucination rate of 36.5%. We further adapt and benchmark four mitigation methods from multiple domains for LALMs. Despite some improvements, effective hallucination mitigation remains an open challenge. Finally, we call on the community to evaluate hallucination and benchmark mitigation methods with MISHAP-Bench.

Momentum

Related topics

Latest papers 14