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- config_name: business
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- config_name: health_and_medicine
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- config_name: humanities_and_social_sciences
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- config_name: science
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- name: source_type
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- config_name: technology_and_engineering
features:
- name: id
dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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num_examples: 2974
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dataset_size: 151127125.93
configs:
- config_name: art_and_design
data_files:
- split: dev
path: art_and_design/dev-*
- split: val
path: art_and_design/val-*
- split: test
path: art_and_design/test-*
- config_name: business
data_files:
- split: dev
path: business/dev-*
- split: val
path: business/val-*
- split: test
path: business/test-*
- config_name: health_and_medicine
data_files:
- split: dev
path: health_and_medicine/dev-*
- split: val
path: health_and_medicine/val-*
- split: test
path: health_and_medicine/test-*
- config_name: humanities_and_social_sciences
data_files:
- split: dev
path: humanities_and_social_sciences/dev-*
- split: val
path: humanities_and_social_sciences/val-*
- split: test
path: humanities_and_social_sciences/test-*
- config_name: science
data_files:
- split: dev
path: science/dev-*
- split: val
path: science/val-*
- split: test
path: science/test-*
- config_name: technology_and_engineering
data_files:
- split: dev
path: technology_and_engineering/dev-*
- split: val
path: technology_and_engineering/val-*
- split: test
path: technology_and_engineering/test-*
CMMMU
π Homepage | π€ Paper | π arXiv | π€ Dataset | GitHub
Introduction
CMMMU includes 12k manually collected multimodal questions from college exams, 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 and comprise 39 highly heterogeneous image types, such as charts, diagrams, maps, tables, music sheets, and chemical structures.
π Mini-Leaderboard
Model | Val (900) | Test (11K) |
---|---|---|
GPT-4V(ision) (Playground) | 42.5 | 43.7 |
Qwen-VL-PLUS* | 39.5 | 36.8 |
Yi-VL-34B | 36.2 | 36.5 |
Yi-VL-6B | 35.8 | 35.0 |
InternVL-Chat-V1.1* | 34.7 | 34.0 |
Qwen-VL-7B-Chat | 30.7 | 31.3 |
SPHINX-MoE* | 29.3 | 29.5 |
InternVL-Chat-ViT-6B-Vicuna-7B | 26.4 | 26.7 |
InternVL-Chat-ViT-6B-Vicuna-13B | 27.4 | 26.1 |
CogAgent-Chat | 24.6 | 23.6 |
Emu2-Chat | 23.8 | 24.5 |
Chinese-LLaVA | 25.5 | 23.4 |
VisCPM | 25.2 | 22.7 |
mPLUG-OWL2 | 20.8 | 22.2 |
Frequent Choice | 24.1 | 26.0 |
Random Choice | 21.6 | 21.6 |
*: results provided by the authors.
Disclaimers
The guidelines for the annotators emphasized strict compliance with copyright and licensing rules from the initial data source, specifically avoiding materials from websites that forbid copying and redistribution. Should you encounter any data samples potentially breaching the copyright or licensing regulations of any site, we encourage you to contact us. Upon verification, such samples will be promptly removed.
Contact
- Ge Zhang: [email protected]
- Wenhao Huang: [email protected]
- Xinrun Du: [email protected]
- Bei Chen: [email protected]
- Jie Fu: [email protected]
Citation
BibTeX:
@article{zhang2024cmmmu,
title={CMMMU: A Chinese Massive Multi-discipline Multimodal Understanding Benchmark},
author={Ge, Zhang and Xinrun, Du and Bei, Chen and Yiming, Liang and Tongxu, Luo and Tianyu, Zheng and Kang, Zhu and Yuyang, Cheng and Chunpu, Xu and Shuyue, Guo and Haoran, Zhang and Xingwei, Qu and Junjie, Wang and Ruibin, Yuan and Yizhi, Li and Zekun, Wang and Yudong, Liu and Yu-Hsuan, Tsai and Fengji, Zhang and Chenghua, Lin and Wenhao, Huang and Jie, Fu},
journal={arXiv preprint arXiv:2401.20847},
year={2024},
}