MMLU-Pro
Challenging benchmark of 12K complex questions for LLM evaluation.
Open dataset Updated 2026-06-18
What is MMLU-Pro?
MMLU-Pro is a dataset of 12K complex questions across various disciplines designed for multi-task understanding benchmarks.
It supports evaluation of large language models by researchers and developers working on question-answering capabilities.
What you can build with MMLU-Pro
LLM capability benchmarking
Run models on the 12K questions to measure accuracy across disciplines and compare results to public leaderboards.
Model error analysis
Inspect incorrect predictions on complex items to identify weaknesses in reasoning or domain knowledge.
Training data augmentation
Use the questions as hard negative or few-shot examples when fine-tuning or prompting newer models.
Load MMLU-Pro
Python
from datasets import load_dataset
ds = load_dataset("TIGER-Lab/MMLU-Pro")- 1pip install datasets
- 2from datasets import load_dataset
- 3ds = load_dataset('TIGER-Lab/MMLU-Pro')
- 4Access splits and columns to run inference
- 5Submit scores to the public leaderboard for comparison
MMLU-Pro: pros & cons
Pros
- +More challenging than original MMLU
- +12K questions spanning many disciplines
- +Direct leaderboard integration
- +Easy HF datasets loading
Cons
- –Evaluation-focused; limited training data
- –Requires strong models to score meaningfully
- –No built-in evaluation script provided
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Frequently asked questions
A harder multi-task benchmark with 12K complex questions designed to test LLMs more rigorously than the original MMLU.
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