Video-MMMU: Evaluating Knowledge Acquisition from Multi-Discipline
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Authors: name: Kairui Hu, name: Penghao Wu, name: Fanyi Pu, name: Wang Xiao, name: Yuanhan Zhang, name: Xiang Yue, name: Bo Li, name: Ziwei Liu

Humans acquire knowledge through three cognitive stages: perceiving
information, comprehending knowledge, and adapting knowledge to solve novel
problems. Videos serve as an effective medium for this learning process,
facilitating a progression through these cognitive stages. However, existing
video benchmarks fail to systematically evaluate the knowledge acquisition
capabilities in Large Multimodal Models (LMMs). To address this gap, we
introduce Video-MMMU, a multi-modal, multi-disciplinary benchmark designed to
assess LMMs' ability to acquire and utilize knowledge from videos. Video-MMMU
features a curated collection of 300 expert-level videos and 900
human-annotated questions across six disciplines, evaluating knowledge
acquisition through stage-aligned question-answer pairs: Perception,
Comprehension, and Adaptation. A proposed knowledge gain metric,
{\Delta}knowledge, quantifies improvement in performance after video viewing.
Evaluation of LMMs reveals a steep decline in performance as cognitive demands
increase and highlights a significant gap between human and model knowledge
acquisition, underscoring the need for methods to enhance LMMs' capability to
learn and adapt from videos.