MMMU: A Massive Multi-Discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
Abstrak
We introduce MMMU: a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning. MMMU includes 11.5K meticulously collected multimodal questions from college exams, quizzes, and text-books, covering six core disciplines: Art & Design, Busi-ness, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering. These questions span 30 subjects and 183 subfields, comprising 30 highly het-erogeneous image types, such as charts, diagrams, maps, tables, music sheets, and chemical structures. Unlike existing benchmarks, MMMU focuses on advanced perception and reasoning with domain-specific knowledge, challenging models to perform tasks akin to those faced by experts. The evaluation of 28 open-source LMMs as well as the propri-etary GPT-4V(ision) and Gemini highlights the substantial challenges posed by MMMU. Even the advanced GPT-4V and Gemini Ultra only achieve accuracies of 56% and 59% respectively, indicating significant room for improvement. We believe MMMU will stimulate the community to build next-generation multimodal foundation models towards expert artificial general intelligence.
Topik & Kata Kunci
Penulis (22)
Xiang Yue
Yuansheng Ni
Kai Zhang
Tianyu Zheng
Ruoqi Liu
Ge Zhang
Samuel Stevens
Dongfu Jiang
Weiming Ren
Yuxuan Sun
Cong Wei
Botao Yu
Ruibin Yuan
Renliang Sun
Ming Yin
Boyuan Zheng
Zhenzhu Yang
Yibo Liu
Wenhao Huang
Huan Sun
Yu Su
Wenhu Chen
Akses Cepat
- Tahun Terbit
- 2023
- Bahasa
- en
- Total Sitasi
- 1965×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1109/CVPR52733.2024.00913
- Akses
- Open Access ✓