Exploring Hallucination of Large Multimodal Models in Video
  Understanding: Benchmark, Analysis and Mitigation
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.
67e3706bc9d8214b5e219149
Hongcheng Gao
67e3706bc9d8214b5e21914a
Jiashu Qu
claimed_verified
2025-03-26T20:44:30.396Z
66b980d66d490d845f8a697e
/avatars/6ab2d08436851063f55baaadae8c4bc0.svg
Joshua Qu
user
Joshua999
67e3706bc9d8214b5e21914b
Jingyi Tang
67e3706bc9d8214b5e21914c
Baolong Bi
claimed_verified
2025-07-29T07:11:59.255Z
642577e06d0f0f5f1dc68904
/avatars/3df7cfcf9ca6cd9c9e9b10a73f4efc35.svg
Bibaolong
user
Bibaolong
67e3706bc9d8214b5e21914d
Yue Liu
67e3706bc9d8214b5e21914e
Hongyu Chen
67e3706bc9d8214b5e21914f
Li Liang
67e3706bc9d8214b5e219150
Li Su
67e3706bc9d8214b5e219151
Qingming Huang
Exploring Hallucination of Large Multimodal Models in Video
  Understanding: Benchmark, Analysis and Mitigation
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.
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.
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