MMLU
MMLU is a reference benchmark for testing language models on multiple choice questions from 57 academic and professional fields.
Description
MMLU (Massive Multitask Language Understanding) is a dataset composed of multiple choice questions from 57 varied disciplines, ranging from the humanities to the exact sciences. Each example includes a question, four answer choices, and the correct option, all structured for a detailed evaluation of language models.
What is this dataset for?
- Evaluate the multitasking abilities of large language models (LLMs)
- Comparing the performance between models on complex and specialized topics
- Building standardized benchmarks for reasoning and comprehension
Can it be enriched or improved?
Yes, it is possible to adapt MMLU to other languages or cultural contexts. New questions can be added by domain, and annotations enriched to refine performance metrics (e.g. difficulty, estimated response time). Multilingual or specialized variants (legal, medical, etc.) could also be developed.
🔎 In summary
🧠 Recommended for
- NLP researchers
- Benchmark designers
- LLM engineers
🔧 Compatible tools
- Hugging Face
- OpenLLM Leaderboard
- PyTorch
- TensorFlow
💡 Tip
Use MMLU as the final benchmark, not for training — this helps to avoid data leaks and to better test true generalization capabilities.
Frequently Asked Questions
What is the MMLU dataset primarily used for?
It is designed to test the multitasking abilities of language models on various domains through multiple choice questions.
Can a model be trained directly on this dataset?
No, MMLU is for evaluation. Training on this corpus would distort the benchmark results.
Is there a multi-lingual version of MMLU?
Not yet, but it is possible to create one by carefully translating questions and adapting cultural references.
