metadata

license: apache-2.0
task_categories:
  - visual-question-answering
language:
  - zh
pretty_name: CMMU
size_categories:
  - 1K<n<10K
dataset_info:
  features:
    - name: type
      dtype: string
    - name: grade_band
      dtype: string
    - name: difficulty
      dtype: string
    - name: question_info
      dtype: string
    - name: split
      dtype: string
    - name: subject
      dtype: string
    - name: image
      dtype: string
    - name: sub_questions
      sequence: string
    - name: options
      sequence: string
    - name: answer
      sequence: string
    - name: solution_info
      dtype: string
    - name: id
      dtype: string
    - name: image
      dtype: image
configs:
  - config_name: default
    data_files:
      - split: val
        path:
          - val/*.parquet

CMMU

📖 Paper | 🤗 Dataset | GitHub

This repo contains the evaluation code for the paper CMMU: A Benchmark for Chinese Multi-modal Multi-type Question Understanding and Reasoning .

We release the validation set of CMMU, you can download it from here. The test set will be hosted on the flageval platform. Users can test by uploading their models.

Introduction

CMMU is a novel multi-modal benchmark designed to evaluate domain-specific knowledge across seven foundational subjects: math, biology, physics, chemistry, geography, politics, and history. It comprises 3603 questions, incorporating text and images, drawn from a range of Chinese exams. Spanning primary to high school levels, CMMU offers a thorough evaluation of model capabilities across different educational stages.

Evaluation Results

We currently evaluated 10 models on CMMU. The results are shown in the following table.

Model	Val Avg.	Test Avg.
InstructBLIP-13b	0.39	0.48
CogVLM-7b	5.55	4.9
ShareGPT4V-7b	7.95	7.63
mPLUG-Owl2-7b	8.69	8.58
LLava-1.5-13b	11.36	11.96
Qwen-VL-Chat-7b	11.71	12.14
Intern-XComposer-7b	18.65	19.07
Gemini-Pro	21.58	22.5
Qwen-VL-Plus	26.77	26.9
GPT-4V	30.19	30.91

Citation

BibTeX:

@article{he2024cmmu,
        title={CMMU: A Benchmark for Chinese Multi-modal Multi-type Question Understanding and Reasoning},
        author={Zheqi He, Xinya Wu, Pengfei Zhou, Richeng Xuan, Guang Liu, Xi Yang, Qiannan Zhu and Hua Huang},
        journal={arXiv preprint arXiv:2401.14011},        
        year={2024},
      }