Contrastive decoding strategies are widely used to reduce object hallucinations in multimodal large language models (MLLMs). These methods work by constructing contrastive samples to induce hallucinations and then suppressing them in the output distribution. However, this paper demonstrates that such approaches fail to effectively mitigate the hallucination problem. The performance improvements observed on POPE Benchmark are largely driven by two misleading factors: (1) crude, unidirectional adjustments to the model's output distribution and (2) the adaptive plausibility constraint, which reduces the sampling strategy to greedy search. To further illustrate these issues, we introduce a series of spurious improvement methods and evaluate their performance against contrastive decoding techniques. Experimental results reveal that the observed performance gains in contrastive decoding are entirely unrelated to its intended goal of mitigating hallucinations. Our findings challenge common assumptions about the effectiveness of contrastive decoding strategies and pave the way for developing genuinely effective solutions to hallucinations in MLLMs.
How to Detect and Defeat Molecular Mirage: A Metric-Driven Benchmark for Hallucination in LLM-based Molecular Comprehension
Shenzhen Graduate School, Peking University（北京大学深圳研究生院）；International Digital Economy Academy (IDEA)（国际数字经济学院（IDEA））；Pengcheng Laboratory（鹏城实验室）；Hong Kong University of Science and Technology（香港科技大学）
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）
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）
