MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark

Xiang Yue, Tianyu Zheng, Yuansheng Ni, Yubo Wang, Kai Zhang, Shengbang Tong, Yuxuan Sun, Botao Yu, Ge Zhang, Huan Sun, Yu Su, Wenhu Chen, Graham Neubig

Keywords: Evaluation, Multimodal Understanding, Multimodal LLMs

Abstract:

This paper introduces MMMU-Pro, a robust version of the Massive Multi-discipline Multimodal Understanding and Reasoning (MMMU) benchmark. MMMU-Pro rigorously assesses multimodal models' true understanding and reasoning capabilities through a three-step process based on MMMU: (1) filtering out questions answerable by text-only models, (2) augmenting candidate options, and (3) introducing a vision-only input setting where questions are embedded within images. This setting challenges AI to truly "see" and "read" simultaneously, testing \textit{a core human cognitive skill of seamlessly integrating visual and textual information}. Results show that model performance is substantially lower on MMMU-Pro than on MMMU, ranging from 16.8\% to 26.9\% across models.
We explore the impact of OCR prompts and Chain of Thought (CoT) reasoning, finding that OCR prompts have minimal effect while CoT generally improves performance. MMMU-Pro provides a more rigorous evaluation tool, closely mimicking real-world scenarios and offering valuable directions for future multimodal research.

Primary Area: datasets and benchmarks

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Submission Number: 11311