Despite recent advances in Large Vision Language Models (LVLMs), these models still suffer from generating hallucinatory responses that do not align with the visual input provided. To mitigate such hallucinations, we introduce Efficient Contrastive Decoding (ECD), a simple method that leverages probabilistic hallucination detection to shift the output distribution towards contextually accurate answers at inference time. By contrasting token probabilities and hallucination scores, ECD subtracts hallucinated concepts from the original distribution, effectively suppressing hallucinations. Notably, our proposed method can be applied to any open-source LVLM and does not require additional LVLM training. We evaluate our method on several benchmark datasets and across different LVLMs. Our experiments show that ECD effectively mitigates hallucinations, outperforming state-of-the-art methods with respect to performance on LVLM benchmarks and computation time.
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）
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）
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）
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）
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）
MedHallTune: An Instruction-Tuning Benchmark for Mitigating Medical Hallucination in Vision-Language Models
The Chinese University of Hong Kong（香港中文大学）；Institute of Medical Intelligence and XR, The Chinese University of Hong Kong（香港中文大学医学智能与XR研究所）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
SeeMe: Mitigating Hallucinations in Large Vision-Language Models through Effective Visual Token Engineering
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University（北京大学计算机科学学院多媒体信息处理国家重点实验室）；University of Pennsylvania（宾夕法尼亚大学）；University of Electronic Science and Technology of China（电子科技大学）；Nanyang Technological University（南洋理工大学）；Tsinghua University（清华大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；De Artificial Intelligence Lab（德人工智能实验室）
CATCH: Complementary Adaptive Token-level Contrastive Decoding to Mitigate Hallucinations in LVLMs
Tsinghua University（清华大学）；Pengcheng Laboratory（鹏城实验室）；Carnegie Mellon University（卡内基梅隆大学）；University of California, Berkeley（加州大学伯克利分校）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）；Chang’an University（长安大学）
Mitigating Entangled Steering in Large Vision-Language Models for Hallucination Reduction
Ministry of Education Key Laboratory of Intelligent Networks and Network Security（教育部智能网络与网络安全重点实验室）；Centre for Frontier AI Research, Institute of High Performance Computing, Agency for Science, Technology and Research（前沿人工智能研究中心，高性能计算研究所，科技研究局）
Mitigating Geospatial Knowledge Hallucination in Large Language Models: Benchmarking and Dynamic Factuality Aligning
College of AI, Tsinghua University（清华大学人工智能学院）；Department of Electronic Engineering, BNRist, Tsinghua University（清华大学电子工程系）；School of Electronic and Information Engineering, Beijing Jiaotong University（北京交通大学电子信息学院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
