metadata

task_categories:
  - multiple-choice
  - question-answering
  - visual-question-answering
language:
  - en
size_categories:
  - 1K<n<10K
configs:
  - config_name: testmini
    data_files:
      - split: testmini
        path: testmini.parquet
  - config_name: testmini_version_split
    data_files:
      - split: testmini_text_only
        path: testmini_text_only.parquet
      - split: testmini_text_lite
        path: testmini_text_lite.parquet
      - split: testmini_text_dominant
        path: testmini_text_dominant.parquet
      - split: testmini_vision_dominant
        path: testmini_vision_dominant.parquet
      - split: testmini_vision_intensive
        path: testmini_vision_intensive.parquet
      - split: testmini_vision_only
        path: testmini_vision_only.parquet
dataset_info:
  - config_name: testmini
    features:
      - name: sample_index
        dtype: string
      - name: problem_index
        dtype: string
      - name: problem_version
        dtype: string
      - name: question
        dtype: string
      - name: image
        dtype: image
      - name: answer
        dtype: string
      - name: question_type
        dtype: string
      - name: metadata
        struct:
          - name: split
            dtype: string
          - name: source
            dtype: string
          - name: subject
            dtype: string
          - name: subfield
            dtype: string
      - name: query_wo
        dtype: string
      - name: query_cot
        dtype: string
    splits:
      - name: testmini
        num_bytes: 166789963
        num_examples: 3940
  - config_name: testmini_version_split
    features:
      - name: sample_index
        dtype: string
      - name: problem_index
        dtype: string
      - name: problem_version
        dtype: string
      - name: question
        dtype: string
      - name: image
        dtype: image
      - name: answer
        dtype: string
      - name: question_type
        dtype: string
      - name: metadata
        struct:
          - name: split
            dtype: string
          - name: source
            dtype: string
          - name: subject
            dtype: string
          - name: subfield
            dtype: string
      - name: query_wo
        dtype: string
      - name: query_cot
        dtype: string
    splits:
      - name: testmini_text_only
        num_bytes: 250959
        num_examples: 788
      - name: testmini_text_lite
        num_examples: 788
      - name: testmini_text_dominant
        num_examples: 788
      - name: testmini_vision_dominant
        num_examples: 788
      - name: testmini_vision_intensive
        num_examples: 788
      - name: testmini_vision_only
        num_examples: 788

Dataset Card for MathVerse

Dataset Description

The capabilities of Multi-modal Large Language Models (MLLMs) in visual math problem-solving remain insufficiently evaluated and understood. We investigate current benchmarks to incorporate excessive visual content within textual questions, which potentially assist MLLMs in deducing answers without truly interpreting the input diagrams.

To this end, we introduce MathVerse, an all-around visual math benchmark designed for an equitable and in-depth evaluation of MLLMs. We meticulously collect 2,612 high-quality, multi-subject math problems with diagrams from publicly available sources. Each problem is then transformed by human annotators into six distinct versions, each offering varying degrees of information content in multi-modality, contributing to 15K test samples in total. This approach allows MathVerse to comprehensively assess whether and how much MLLMs can truly understand the visual diagrams for mathematical reasoning.

Six different versions of each problem in MathVerse transformed by expert annotators.

In addition, we propose a Chain-of-Thought (CoT) Evaluation strategy for a fine-grained assessment of the output answers. Rather than naively judging True or False, we employ GPT-4(V) to adaptively extract crucial reasoning steps, and then score each step with detailed error analysis, which can reveal the intermediate CoT reasoning quality by MLLMs.

The two phases of the CoT evaluation strategy.

Paper Information

Code: https://github.com/ZrrSkywalker/MathVerse
Project: https://mathverse-cuhk.github.io/
Visualization: https://mathverse-cuhk.github.io/#visualization
Leaderboard: https://mathverse-cuhk.github.io/#leaderboard
Paper: https://arxiv.org/abs/2403.14624

Dataset Examples

🖱 Click to expand the examples for six problems versions within three subjects

🔍 Plane Geometry

🔍 Solid Geometry

🔍 Functions

Leaderboard

Contributing to the Leaderboard

🚨 The Leaderboard is continuously being updated.

The evaluation instructions and tools will be released soon. For now, please send your results on the testmini set to this email: [email protected]. Please refer to the following template to prepare your result json file.

output_testmini_template.json

Citation

If you find MathVerse useful for your research and applications, please kindly cite using this BibTeX:

@inproceedings{zhang2024mathverse,
  title={MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?},
  author={Renrui Zhang, Dongzhi Jiang, Yichi Zhang, Haokun Lin, Ziyu Guo, Pengshuo Qiu, Aojun Zhou, Pan Lu, Kai-Wei Chang, Peng Gao, Hongsheng Li},
  booktitle={arXiv},
  year={2024}
}