VLM Hallucination Mitigation

VLM: Vision-Language Model

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11 papers in the last four weeks, down 15% on the four weeks before. 0.1% of all new papers.

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Aligning with Your Own Voice: Self-Corrected Preference Learning for Hallucination Mitigation in LVLMs
Apr 27, 2026Byeonggeuk Lim, JungMin Yun, Junehyoung Kwon +2Vision-Language Models Hallucination in Language Models
Graduate School of Advanced Imaging Sciences, Multimedia and Film, Chung-Ang University · Department of Artificial Intelligence, Chung-Ang University
When Prompts Override Vision: Prompt-Induced Hallucinations in LVLMs
Apr 23, 2026Pegah Khayatan, Jayneel Parekh, Arnaud Dapogny +3VLM Evaluation LLM Hallucination Mitigation
1ISIR, Sorbonne Universit´e, Paris, France · 2Valeo.ai, Paris, France
R-CoV: Region-Aware Chain-of-Verification for Alleviating Object Hallucinations in LVLMs
Apr 22, 2026Jiahao Xie, Alessio Tonioni, Nathalie Rauschmayr +2Hallucination Detection in VLMs Vision-Language Models
Max Planck Institute for Informatics, SIC · VIA Research Center · Google
Breaking the Illusion: When Positive Meets Negative in Multimodal Decoding
Apr 22, 2026Yubo Jiang, Yitong An, Xin Yang +7Vision-Language Models Hallucination in Language Models
School of Astronautics, Beihang University, Beijing 102206, China · Longcat Interaction Team, Meituan, Beijing 100102, China · Tianmushan Laboratory, Beihang University, Hangzhou 311115, China
Mitigating Hallucinations in Large Vision-Language Models without Performance Degradation
Apr 22, 2026Xingyu Zhu, Junfeng Fang, Shuo Wang +4Vision-Language Models VLM Adaptation
MoE Key Lab of BIPC, University of Science and Technology of China · National University of Singapore
VCE: A zero-cost hallucination mitigation method of LVLMs via visual contrastive editing
Apr 21, 2026Yanbin Huang, Yisen Li, Guiyao Tie +6Large Vision-Language Models Object Hallucination in VLMs
1Huazhong University of Science and Technology · Institute of Artificial Intelligence (TeleAI), China Telecom
Mitigating Multimodal Hallucination via Phase-wise Self-reward
Apr 20, 2026Yu Zhang, Chuyang Sun, Kehai Chen +3Vision-Language Models Hallucination in Language Models
Harbin Institute of Technology · Peng Cheng Laboratory · Shenzhen, China
Attention at Rest Stays at Rest: Breaking Visual Inertia to Mitigate Relation Hallucinations
Apr 2, 2026Boyang Gong, Yu Zheng, Fanye Kong +2VLM Hallucination Mitigation Vision-Language Grounding
Tsinghua University
Revealing Multi-View Hallucination in Large Vision-Language Models
Mar 25, 2026Wooje Park, Insu Lee, Soohyun Kim +4Visual Question Answering Hallucination in Language Models
Seoul National University · Sungkyunkwan University
SAVER: Mitigating Hallucinations in Large Vision-Language Models via Style-Aware Visual Early Revision
Aug 5, 2025Zhaoxu Li, Chenqi Kong, Yi Yu +6VLM Evaluation LLM Hallucination Mitigation
ROSE Lab, Interdisciplinary Graduate Programme, Nanyang Technological University, Singapore · ROSE Lab, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore · City University of Hong Kong, Hong Kong SAR +3
Mitigating Hallucinations via Inter-Layer Consistency Aggregation in Large Vision-Language Models
May 18, 2025Kai Tang, Jinhao You, Yichen Guo +8Vision-Language Models Hallucination in Language Models
Peking University · University of Pennsylvania · Nanyang Technological University +4

Apr 27, 2026, cs.AIJ/K move · Enter open · S save

Aligning with Your Own Voice: Self-Corrected Preference Learning for Hallucination Mitigation in LVLMs

Byeonggeuk Lim, JungMin Yun, Junehyoung Kwon, Kyeonghyun Kim+1

Graduate School of Advanced Imaging Sciences, Multimedia and Film, Chung-Ang University · Department of Artificial Intelligence, Chung-Ang University

Vision-Language Models Hallucination in Language Models VLM Hallucination VLM Hallucination Mitigation

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Large Vision-Language Models (LVLMs) frequently suffer from hallucinations. Existing preference learning-based approaches largely rely on proprietary models to construct preference datasets. We identify that this reliance introduces a distributional mismatch between the proprietary and target models that hinders efficient alignment. To address this, we propose Alignment via VErified Self-correction DPO (AVES-DPO), a framework that aligns LVLMs using in-distribution data derived from the model's intrinsic knowledge. Our approach employs a consensus-based verification mechanism to diagnose diverse hallucinations and guides the model to self-correct, thereby generating preference pairs strictly compatible with its internal distribution. Extensive experiments demonstrate that AVES-DPO surpasses existing baselines in hallucination mitigation while requiring only 5.2k samples.

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