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As the capabilities of large multimodal models (LMMs) continue to advance,evaluating the performance of LMMs emerges as an increasing need. Additionally,there is an even larger gap in evaluating the advanced knowledge and reasoningabilities of LMMs in non-English contexts such as Chinese. We introduce CMMMU,a new Chinese Massive Multi-discipline Multimodal Understanding benchmarkdesigned to evaluate LMMs on tasks demanding college-level subject knowledgeand deliberate reasoning in a Chinese context. CMMMU is inspired by andstrictly follows the annotation and analysis pattern of MMMU.  CMMMU includes 12k manually collected multimodal questions from collegeexams, quizzes, and textbooks, covering six core disciplines: Art & Design,Business, Science, Health & Medicine, Humanities & Social Science, and Tech &Engineering, like its companion, MMMU. These questions span 30 subjects andcomprise 39 highly heterogeneous image types, such as charts, diagrams, maps,tables, music sheets, and chemical structures.  CMMMU focuses on complex perception and reasoning with domain-specificknowledge in the Chinese context. We evaluate 11 open-source LLMs and oneproprietary GPT-4V(ision). Even GPT-4V only achieves accuracies of 42%,indicating a large space for improvement. CMMMU will boost the community tobuild the next-generation LMMs towards expert artificial intelligence andpromote the democratization of LMMs by providing diverse language contexts.

CMMMU: A Chinese Massive Multi-discipline Multimodal Understanding Benchmark