Multimodal Large Language Models (MLLMs) frequently exhibit hallucination phenomena, but the underlying reasons remain poorly understood. In this paper, we present an empirical analysis and find that, although MLLMs incorrectly generate the objects in the final output, they are actually able to recognize visual objects in the preceding layers. We speculate that this may be due to the strong knowledge priors of the language model suppressing the visual information, leading to hallucinations. Motivated by this, we propose a novel dynamic correction decoding method for MLLMs DeCo, which adaptively selects the appropriate preceding layers and proportionally integrates knowledge into the final layer to adjust the output logits. Note that DeCo is model agnostic and can be seamlessly incorporated with various classic decoding strategies and applied to different MLLMs. We evaluate DeCo on widely-used benchmarks, demonstrating that it can reduce hallucination rates by a large margin compared to baselines, highlighting its potential to mitigate hallucinations. Code is available at https://github.com/zjunlp/DeCo.
ClinHallu: A Benchmark for Diagnosing Stage-Wise Hallucinations in Medical MLLM Reasoning
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；DAMO Academy, Alibaba Group（阿里巴巴达摩院）；Hupan Lab（湖畔实验室）；Zhejiang University（浙江大学）
HalluShift++: Bridging Language and Vision through Internal Representation Shifts for Hierarchical Hallucinations in MLLMs
Netaji Subhash Engineering College (NSEC)（奈尔贾伊·萨布哈工程学院）；TCG Crest；Electronics and Communication Sciences Unit (ECSU)（电子与通信科学单位）；Indian Statistical Institute（印度统计研究所）
EmotionHallucer: Evaluating Emotion Hallucinations in Multimodal Large Language Models
Lappeenranta-Lahti University of Technology LUT（拉佩兰塔-拉赫蒂技术大学）；University of Oulu（奥卢大学）；Southeast University（东南大学）；Shanghai Jiao Tong University（上海交通大学）；Brno University of Technology（布拉格技术大学）
ADAPT: Attention Dynamics Alignment with Preference Tuning for Faithful MLLMs
University of Science and Technology of China（中国科学技术大学）；Huawei Technologies Ltd.（华为技术有限公司）；State Key Laboratory of Communication Content Cognition, People’s Daily Online（人民网传播内容认知国家重点实验室）
Mitigating Visual Hallucinations via Semantic Curriculum Preference Optimization in MLLMs
School of Engineering, Westlake University, Hangzhou, China（西湖大学工程学院）；School of Cyberspace Security, Nanjing University of Science and Technology, Nanjing, China（南京理工大学网络安全学院）
COPO: Causal-Oriented Policy Optimization for Hallucinations of MLLMs
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Software Chinese Academy of Sciences（中国科学院软件研究所）；Wangxuan Institute of Computer Technology, Peking University（北京大学王轩计算机技术研究所）；Amazon.com, Inc.（亚马逊公司）
In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation
City University of Hong Kong（香港城市大学）；National University of Singapore（新加坡国立大学）；Shanghai Jiao Tong University（上海交通大学）；Stanford University（斯坦福大学）；Penn State University（宾夕法尼亚州立大学）；Hong Kong University of Science and Technology（香港科技大学）
Tell Model Where to Look: Mitigating Hallucinations in MLLMs by Vision-Guided Attention
School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；Zhongguancun Academy（中关村学院）；Southeast Academy of Information Technology, Beijing Institute of Technology（北京理工大学信息科技东南学院）；Zhongguancun Laboratory（中关村实验室）
FINER: MLLMs Hallucinate under Fine-grained Negative Queries
Technical University of Munich（慕尼黑技术大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Helmholtz Munich（海德堡-慕尼黑亥姆霍尔茨中心）；Google（谷歌）；LTCI, Télécom Paris, Institut Polytechnique de Paris（LTCI，巴黎电信学院，巴黎理工学院）
Robust Multimodal Large Language Models Against Modality Conflict
School of Artificial Intelligence（人工智能学院）；Data Science, University of Science（数据科学，科学大学）；Department of Electronic Engineering（电子工程系）；Information Science, University of Science（信息科学，科学大学）；Huawei Technologies Co., Ltd.（华为技术有限公司）
Combating Multimodal LLM Hallucination via Bottom-Up Holistic Reasoning
National University of Singapore（新加坡国立大学）；University of Arizona（亚利桑那大学）；University of California, Santa Barbara（加州大学圣巴巴拉分校）；Skywork AI（天工智能）；Nanyang Technological University（南洋理工大学）
HaloProbe: Bayesian Detection and Mitigation of Object Hallucinations in Vision-Language Models
Multimodal AI Lab, Technical University of Darmstadt（达姆施塔特工业大学多模态人工智能实验室）；Department of Computer Engineering, Sharif University of Technology（谢里夫理工大学计算机工程系）
