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

TL;DR

Video-MMMU introduces a comprehensive benchmark to evaluate how well large multimodal models acquire and utilize knowledge from videos across multiple disciplines, highlighting current limitations and gaps in model learning capabilities.

Contribution

The paper presents Video-MMMU, a new multi-disciplinary benchmark with a knowledge gain metric to systematically assess LMMs' knowledge acquisition from videos.

Findings

LMMs' performance declines with increasing cognitive demands

Humans outperform models in knowledge acquisition tasks

Significant gap exists between human and model learning from videos

Abstract

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.