Exploring Hallucination of Large Multimodal Models in Video
  Understanding: Benchmark, Analysis and Mitigation
Authors: name: Hongcheng Gao, name: Jiashu Qu, name: Jingyi Tang, name: Baolong Bi, name: Yue Liu, name: Hongyu Chen, name: Li Liang, name: Li Su, name: Qingming Huang

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.