MMLU (Massive Multitask Language Understanding)
The gold-standard multi-subject benchmark: 16k multiple-choice questions spanning 57 subjects from STEM and humanities to law, medicine, and social sciences. Every major LLM is evaluated on MMLU. A model scoring >80% is considered strong; GPT-4 scores ~87%, Llama 3 70B ~82%.
from datasets import load_dataset
ds = load_dataset("cais/mmlu")Preview a sample row
{
"question": "A train travels 120 km in 2 hours. What is its average speed?",
"subject": "high_school_mathematics",
"choices": ["40 km/h", "60 km/h", "80 km/h", "120 km/h"],
"answer": "B"
}Frequently asked questions
Can I use MMLU (Massive Multitask Language Understanding) commercially?
Yes — MMLU (Massive Multitask Language Understanding) is released under MIT, a permissive license that allows commercial use, including training models you ship in a product. Check the dataset card for attribution requirements before release.
How much data does MMLU (Massive Multitask Language Understanding) contain, and do I need all of it?
MMLU (Massive Multitask Language Understanding) contains 16k Questions. It is an evaluation benchmark, so it is used in full to measure models — never mix it into training data, or your benchmark scores become meaningless.
What is MMLU (Massive Multitask Language Understanding) best used for?
Benchmarking general knowledge - never train on it. It belongs to the Evaluation & Benchmarks section of our dataset hub, where you'll find alternatives and complementary sets.