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MMMU: Massive Multi-discipline Multimodal Understanding

Multi-discipline multimodal QA requiring college-level understanding. Annotators answer multiple-choice questions that require interpreting images (charts, diagrams, photos) along with text across 30 subjects spanning STEM, humanities, social sciences, and more.

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Configuration Fileconfig.yaml

This Potato config reproduces the annotation task. Save it as config.yaml and run potato start config.yaml to try it.

yaml
# MMMU: Massive Multi-discipline Multimodal Understanding
# Based on Yue et al., CVPR 2024
# Paper: https://openaccess.thecvf.com/content/CVPR2024/papers/Yue_MMMU_CVPR_2024_paper.pdf
#
# Multi-discipline multimodal QA benchmark requiring college-level subject
# knowledge and reasoning over images (charts, diagrams, photos) and text
# across 30 subjects including STEM, humanities, and social sciences.
#
# Annotation Guidelines:
# 1. Read the question carefully and examine the associated image
# 2. Consider the subject area and apply relevant domain knowledge
# 3. Select the best answer choice from the provided options
# 4. Use the image content to inform your answer — many questions
#    cannot be answered from text alone

annotation_task_name: "MMMU: Multimodal Understanding"
task_dir: "."

data_files:
  - sample-data.json
item_properties:
  id_key: "id"
  text_key: "text"

output_annotation_dir: "annotation_output/"
output_annotation_format: "json"

annotation_schemes:
  # Step 1: Select the correct answer
  - annotation_type: radio
    name: answer
    description: "Select the best answer to the question based on the image and text."
    labels:
      - "A"
      - "B"
      - "C"
      - "D"
    keyboard_shortcuts:
      "A": "a"
      "B": "b"
      "C": "c"
      "D": "d"
    tooltips:
      "A": "Select option A"
      "B": "Select option B"
      "C": "Select option C"
      "D": "Select option D"

  # Step 2: Rate difficulty
  - annotation_type: radio
    name: difficulty
    description: "How difficult was this question?"
    labels:
      - "Easy"
      - "Medium"
      - "Hard"
    tooltips:
      "Easy": "Answer is straightforward with basic subject knowledge"
      "Medium": "Requires moderate reasoning or domain expertise"
      "Hard": "Requires deep expertise or multi-step reasoning"

  # Step 3: Confidence
  - annotation_type: radio
    name: confidence
    description: "How confident are you in your answer?"
    labels:
      - "Very confident"
      - "Somewhat confident"
      - "Not confident"

html_layout: |
  <div style="margin-bottom: 15px; padding: 10px; background: #f0f4f8; border-radius: 6px;">
    <strong>Subject:</strong> {{subject}} &mdash; <strong>Subfield:</strong> {{subfield}}
  </div>
  <div style="text-align: center; margin-bottom: 15px;">
    <img src="{{image_url}}" style="max-width: 100%; max-height: 500px; border: 1px solid #ddd; border-radius: 4px;" />
  </div>
  <div style="font-size: 16px; line-height: 1.6; margin-bottom: 15px;">
    <strong>Question:</strong> {{text}}
  </div>
  <div style="padding: 10px; background: #fafafa; border-radius: 6px; line-height: 1.8;">
    <strong>Options:</strong><br/>
    {{options}}
  </div>

allow_all_users: true
instances_per_annotator: 100
annotation_per_instance: 3
allow_skip: true
skip_reason_required: false

Sample Datasample-data.json

json
[
  {
    "id": "mmmu_001",
    "text": "A patient presents with the ECG tracing shown in the image. Which of the following is the most likely diagnosis?",
    "image_url": "https://example.com/mmmu/ecg_tracing_001.png",
    "options": "A) Atrial fibrillation\nB) Ventricular tachycardia\nC) Second-degree AV block (Mobitz Type I)\nD) Normal sinus rhythm",
    "subject": "Clinical Medicine",
    "subfield": "Cardiology"
  },
  {
    "id": "mmmu_002",
    "text": "Based on the circuit diagram shown, what is the total resistance between terminals A and B?",
    "image_url": "https://example.com/mmmu/circuit_diagram_002.png",
    "options": "A) 10 ohms\nB) 15 ohms\nC) 20 ohms\nD) 25 ohms",
    "subject": "Electrical Engineering",
    "subfield": "Circuit Analysis"
  }
]

// ... and 8 more items

Get This Design

View on GitHub

Clone or download from the repository

Quick start:

git clone https://github.com/davidjurgens/potato-showcase.git
cd potato-showcase/multimodal/mmmu-multimodal-understanding
potato start config.yaml

Dataset & paper

Yue et al., CVPR 2024

Citation (BibTeX)

bibtex
@inproceedings{yue2024mmmu,
    title = "{MMMU}: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert {AGI}",
    author = "Yue, Xiang  and Ni, Yuansheng  and Zhang, Kai  and Zheng, Tianyu  and Liu, Ruoqi  and Zhang, Ge  and Stevens, Samuel  and Jiang, Dongfu  and Ren, Weiming  and Sun, Yuxuan  and Wei, Cong  and Yu, Botao  and Yuan, Ruibin  and Sun, Renliang  and Yin, Ming  and Zheng, Boyuan  and Yang, Zhenzhu  and Liu, Yibo  and Huang, Wenhao  and Sun, Huan  and Su, Yu  and Chen, Wenhu",
    booktitle = "Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
    year = "2024",
    url = "https://arxiv.org/abs/2311.16502"
}

Details

Annotation Types

radio

Domain

MultimodalEducationReasoning

Use Cases

Visual Question AnsweringMultimodal ReasoningEducational Assessment

Tags

mmmumultimodalvqareasoningeducationcvpr2024multi-disciplinecollege-level

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