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Luyuan Chen

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6

AAAI Conference 2025 Conference Paper

MHBench: Demystifying Motion Hallucination in VideoLLMs

  • Ming Kong
  • Xianzhou Zeng
  • Luyuan Chen
  • Yadong Li
  • Bo Yan
  • Qiang Zhu

Similar to Language or Image LLMs, VideoLLMs are also plagued by hallucination issues. Hallucinations in videos not only manifest in the spatial dimension regarding the perception of the existence of visual objects (static) but also the temporal dimension influencing the perception of actions and events (dynamic). This paper introduces the concept of Motion Hallucination for the first time, exploring the hallucination phenomena caused by insufficient motion perception capabilities in VideoLMMs, as well as how to detect, evaluate, and mitigate the hallucination. To this end, we propose the first benchmark for assessing motion hallucination MHBench, which consists of 1,200 videos of 20 different action categories. By constructing a collection of adversarial triplet types of videos (original/antonym/incomplete), we achieve a comprehensive evaluation of motion hallucination. Furthermore, we present a Motion Contrastive Decoding (MotionCD) method, which employs bidirectional motion elimination between the original video and its reverse playback to construct an amateur model that removes the influence of motion while preserving visual information, thereby effectively suppressing motion hallucination. Extensive experiments on MHBench reveal that current state-of-the-art VideoLLMs significantly suffer from motion hallucination, while the introduction of MotionCD can effectively mitigate this issue, achieving up to a 15.1% performance improvement. We hope this work will guide future efforts in avoiding and mitigating hallucinations in VideoLLMs.

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AAAI Conference 2025 Conference Paper

MoLE:Decoding by Mixture of Layer Experts Alleviates Hallucination in Large Vision-Language Models

  • Tian Liang
  • Yuetian Du
  • Jing Huang
  • Ming Kong
  • Luyuan Chen
  • Yadong Li
  • Siye Chen
  • Qiang Zhu

Recent advancements in Large Vision-Language Models (LVLMs) highlight their ability to integrate and process multi-modal information. However, hallucinations—where generated content is inconsistent with input vision and instructions—remain a challenge. In this paper, we analyze LVLMs' layer-wise decoding and identify that hallucinations can arise during the reasoning and factual information injection process. Additionally, as the number of generated tokens increases, the forgetting of the original prompt may also lead to hallucinations.To address this, we propose a training-free decoding method called Mixture of Layer Experts (MoLE). MoLE leverages a heuristic gating mechanism to dynamically select multiple layers of LVLMs as expert layers: the Final Expert, the Second Opinion expert, and the Prompt Retention Expert. By the cooperation of each expert, MoLE enhances the robustness and faithfulness of the generation process. Our extensive experiments demonstrate that MoLE significantly reduces hallucinations, outperforming the current state-of-the-art decoding techniques across three mainstream LVLMs and two established hallucination benchmarks. Moreover, our method reveals the potential of LVLMs to independently produce more reliable and accurate outputs.

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