title: Exploring Hallucination of Large Multimodal Models in Video
    Understanding: Benchmark, Analysis and Mitigation
summary: The hallucination of large multimodal models (LMMs), providing responses that
  appear correct but are actually incorrect, limits their reliability and
  applicability. This paper aims to study the hallucination problem of LMMs in
  video modality, which is dynamic and more challenging compared to static
  modalities like images and text. From this motivation, we first present a
  comprehensive benchmark termed HAVEN for evaluating hallucinations of LMMs in
  video understanding tasks. It is built upon three dimensions, i.e.,
  hallucination causes, hallucination aspects, and question formats, resulting in
  6K questions. Then, we quantitatively study 7 influential factors on
  hallucinations, e.g., duration time of videos, model sizes, and model
  reasoning, via experiments of 16 LMMs on the presented benchmark. In addition,
  inspired by recent thinking models like OpenAI o1, we propose a video-thinking
  model to mitigate the hallucinations of LMMs via supervised reasoning
  fine-tuning (SRFT) and direct preference optimization (TDPO)-- where SRFT
  enhances reasoning capabilities while TDPO reduces hallucinations in the
  thinking process. Extensive experiments and analyses demonstrate the
  effectiveness. Remarkably, it improves the baseline by 7.65% in accuracy on
  hallucination evaluation and reduces the bias score by 4.5%. The code and data
  are public at https://github.com/Hongcheng-Gao/HAVEN.
paper_authors._id: 67e3706bc9d8214b5e219149
paper_authors.name: Hongcheng Gao
paper_authors._id: 67e3706bc9d8214b5e21914a
paper_authors.name: Jiashu Qu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-26T20:44:30.396Z
paper_authors._id: 66b980d66d490d845f8a697e
paper_authors.avatarUrl: /avatars/6ab2d08436851063f55baaadae8c4bc0.svg
paper_authors.fullname: Joshua Qu
paper_authors.type: user
paper_authors.user: Joshua999
paper_authors._id: 67e3706bc9d8214b5e21914b
paper_authors.name: Jingyi Tang
paper_authors._id: 67e3706bc9d8214b5e21914c
paper_authors.name: Baolong Bi
paper_authors._id: 67e3706bc9d8214b5e21914d
paper_authors.name: Yue Liu
paper_authors._id: 67e3706bc9d8214b5e21914e
paper_authors.name: Hongyu Chen
paper_authors._id: 67e3706bc9d8214b5e21914f
paper_authors.name: Li Liang
paper_authors._id: 67e3706bc9d8214b5e219150
paper_authors.name: Li Su
paper_authors._id: 67e3706bc9d8214b5e219151
paper_authors.name: Qingming Huang
paper_title: Exploring Hallucination of Large Multimodal Models in Video
    Understanding: Benchmark, Analysis and Mitigation
paper_summary: The hallucination of large multimodal models (LMMs), providing responses that
  appear correct but are actually incorrect, limits their reliability and
  applicability. This paper aims to study the hallucination problem of LMMs in
  video modality, which is dynamic and more challenging compared to static
  modalities like images and text. From this motivation, we first present a
  comprehensive benchmark termed HAVEN for evaluating hallucinations of LMMs in
  video understanding tasks. It is built upon three dimensions, i.e.,
  hallucination causes, hallucination aspects, and question formats, resulting in
  6K questions. Then, we quantitatively study 7 influential factors on
  hallucinations, e.g., duration time of videos, model sizes, and model
  reasoning, via experiments of 16 LMMs on the presented benchmark. In addition,
  inspired by recent thinking models like OpenAI o1, we propose a video-thinking
  model to mitigate the hallucinations of LMMs via supervised reasoning
  fine-tuning (SRFT) and direct preference optimization (TDPO)-- where SRFT
  enhances reasoning capabilities while TDPO reduces hallucinations in the
  thinking process. Extensive experiments and analyses demonstrate the
  effectiveness. Remarkably, it improves the baseline by 7.65% in accuracy on
  hallucination evaluation and reduces the bias score by 4.5%. The code and data
  are public at https://github.com/Hongcheng-Gao/HAVEN.
paper_ai_summary: A comprehensive benchmark called HAVEN assesses hallucinations in video understanding tasks, and a video-thinking model using supervised reasoning fine-tuning and preference optimization reduces hallucinations and improves model accuracy.
paper_ai_keywords: hallucination large multimodal models video modality HAVEN benchmark hallucination causes hallucination aspects question formats duration time model sizes model reasoning video-thinking model supervised reasoning fine-tuning direct preference optimization