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.
Configuration Fileconfig.yaml
This Potato config reproduces the annotation task. Save it as config.yaml and run potato start config.yaml to try it.
# 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}} — <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
[
{
"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 itemsGet This Design
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)
@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
Domain
Use Cases
Tags
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