Video-MMMU: Evaluating Knowledge Acquisition from Multi-Discipline Professional Videos

Kairui, Penghao Wu, Pu Fanyi, Wang Xiao, Zhang Yuanhan, Xiang Yue, Bo Li, Ziwei Liu

Video-MMMU: Evaluating Knowledge Acquisition from Multi-Discipline Professional Videos: 24 upvotes on Hugging Face Daily Papers, #5 of 17 papers on 2025-01-24. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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