Recently, multimodal large language models (MLLMs) have demonstrated remarkable performance in visual-language tasks. However, the authenticity of the responses generated by MLLMs is often compromised by object hallucinations. We identify that a key cause of these hallucinations is the model's over-susceptibility to specific image frequency features in detecting objects. In this paper, we introduce Multi-Frequency Perturbations (MFP), a simple, cost-effective, and pluggable method that leverages both low-frequency and high-frequency features of images to perturb visual feature representations and explicitly suppress redundant frequency-domain features during inference, thereby mitigating hallucinations. Experimental results demonstrate that our method significantly mitigates object hallucinations across various model architectures. Furthermore, as a training-time method, MFP can be combined with inference-time methods to achieve state-of-the-art performance on the CHAIR benchmark.
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）
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）
Mitigating Behavioral Hallucination in Multimodal Large Language Models for Sequential Images
Provable Responsible AI and Data Analytics (PRADA) Lab（可证明负责任的人工智能与数据分析实验室）；King Abdullah University of Science and Technology（国王阿卜杜勒阿齐兹大学）；University of Electronic Science and Technology of China（中国电子科学技术大学）；University of Copenhagen（哥本哈根大学）
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）
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）
MHSA: A Lightweight Framework for Mitigating Hallucinations via Steered Attention in LVLMs
Tsinghua University（清华大学）；Tsinghua University, Tencent（清华大学腾讯）；Tencent（腾讯）；University of Science and Technology Beijing（北京科技大学）；University of Macau（澳门大学）
Mitigating Multilingual Hallucination in Large Vision-Language Models
School of Computer Science & Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）；School of Computing Science and Technology, Fudan University（复旦大学计算机科学技术学院）；Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；School of Computing Science and Technology, Zhejiang Gongshang University（浙江工商大学计算机科学与技术学院）；Department of Computer Science and Engineering, The Chinese University of Hong Kong（香港中文大学计算机科学与工程学系）
Prescribing the Right Remedy: Mitigating Hallucinations in Large Vision-Language Models via Targeted Instruction Tuning
Beijing Key Lab of Traffic Data Analysis and Mining（北京交通大数据分析与挖掘重点实验室）；Beijing Jiaotong University（北京交通大学）；School of Information Technology and Management（信息科学技术学院）
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）
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）
Mitigating Hallucination in Large Language Models: A Capability-Oriented Survey on RAG, Reasoning, and Agentic Systems
Electronic Information School, Wuhan University（武汉大学电子信息学院）；School of Computing, Dublin City University（都柏林城市大学计算机学院）；School of Public Health, Wuhan University（武汉大学公共卫生学院）；Insight Centre for Data Analytics, University of Galway（Galway大学数据分析研究中心）；Insight Centre for Data Analytics, Dublin City University（都柏林城市大学数据分析研究中心）
