Skip to content
Showcase/MMMU: Massive Multi-discipline Multimodal Understanding
intermediatetext

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.

Submit

配置文件config.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

示例数据sample-data.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

获取此设计

View on GitHub

Clone or download from the repository

快速开始:

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

详情

标注类型

radio

领域

MultimodalEducationReasoning

应用场景

Visual Question AnsweringMultimodal ReasoningEducational Assessment

标签

mmmumultimodalvqareasoningeducationcvpr2024multi-disciplinecollege-level

发现问题或想改进此设计?

提交 Issue