Multimodal Large Language Models (MLLMs) often generate hallucinations, where the output deviates from the visual content. Given that these hallucinations can take diverse forms, detecting hallucinations at a fine-grained level is essential for comprehensive evaluation and analysis. To this end, we propose a novel task of multimodal fine-grained hallucination detection and editing for MLLMs. Moreover, we propose ZINA, a novel method that identifies hallucinated spans at a fine-grained level, classifies their error types into six categories, and suggests appropriate refinements. To train and evaluate models for this task, we construct VisionHall, a dataset comprising 6.9k outputs from twelve MLLMs manually annotated by 211 annotators, and 20k synthetic samples generated using a graph-based method that captures dependencies among error types. We demonstrated that ZINA outperformed existing methods, including GPT-4o and Llama-3.2, in both detection and editing tasks.
HIME: Mitigating Object Hallucinations in LVLMs via Hallucination Insensitivity Model Editing
School of Information and Communication Technology, Griffith University, Australia（信息与通信技术学院，格里菲斯大学）；Data61, CSIRO, Australia（Data61，澳大利亚联邦科学与工业研究组织）；School of Engineering, Macquarie University, Sydney, Australia（工程学院，麦觉大学）；School of Information Systems, Queensland University of Technology, Australia（信息系统学院，昆士兰技术大学）
TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Key Laboratory of AI Safety, Chinese Academy of Sciences（中国科学院人工智能安全重点实验室）
Beyond Global Editing: Per-Instance Disentangled Subspaces for Training-Free Hallucination Mitigation in LVLMs
Macquarie University（麦考瑞大学）；York University（约克大学）；Northern Illinois University（北伊利诺伊大学）；University of Technology Sydney（悉尼科技大学）；North South University（北南大学）；Lancaster University（兰卡斯特大学）；Australian National University（澳大利亚国立大学）
HulluEdit: Single-Pass Evidence-Consistent Subspace Editing for Mitigating Hallucinations in Large Vision-Language Models
Beijing University of Posts and Telecommunications（北京邮电大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Beijing Jiaotong University（北京交通大学）
Image Tokens Matter: Mitigating Hallucination in Discrete Tokenizer-based Large Vision-Language Models via Latent Editing
Hasso Plattner Institute（霍普夫纳研究所）；University of Potsdam（波茨坦大学）；University of Cambridge（剑桥大学）；University of Oxford（牛津大学）；German University of Digital Science（德国数字科学大学）
Editing Factual Knowledge and Explanatory Ability of Medical Large Language Models
University of Science and Technology of China（中国科学技术大学）；City University of Hong Kong（香港城市大学）；Jarvis Research Center（贾维斯研究中心）；Tencent YouTu Lab（腾讯优图实验室）；Peking University（北京大学）
Constraining Sequential Model Editing with Editing Anchor Compression
National Engineering Research Center of Speech and Language Information Processing, University of Science and Technology of China（中国科学技术大学语音及语言信息处理国家工程研究中心）；Zhejiang University（浙江大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
BEAF: Observing BEfore-AFter Changes to Evaluate Hallucination in Vision-language Models
POSTECH（浦项科技大学）；Grad. School of AI, POSTECH（浦项科技大学人工智能学院）；Institute for Convergence Research and Education in Advanced Technology, Yonsei University（延世大学尖端技术融合研究教育学院）
Cognitive Pivot Points and Visual Anchoring: Unveiling and Rectifying Hallucinations in Multimodal Reasoning Models
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学 龙城人工智能学院）；South China Agricultural University（华南农业大学）；South China Normal University（华南师范大学）；Monash University（莫纳什大学）；Jishou University（吉首大学）；Hong Kong University of Science and Technology（香港科技大学）；Tsinghua University（清华大学）
Beyond Textual CoT: Interleaved Text-Image Chains with Deep Confidence Reasoning for Image Editing
Shanghai Jiaotong University（上海交通大学）；Huawei Noah’s Ark Lab（华为诺亚实验室）；East China Normal University（华东师范大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）
