MMLU: Measuring Massive Multitask Language Understanding

Knowledge

Evaluate models on 57 tasks including elementary mathematics, US history, computer science, law, and more.

Contributed By

@jjallaire

Overview

MMLU is a benchmark to measure the model’s multitask accuracy. This dataset covers 57 tasks such as elementary mathematics, US history, computer science, law, and more.

Usage

First, install the inspect_ai and inspect_evals Python packages with:

pip install inspect_ai
pip install git+https://github.com/UKGovernmentBEIS/inspect_evals

Then, evaluate against one more models with:

inspect eval inspect_evals/mmlu --model openai/gpt-4o

If you don’t want to specify the --model each time you run an evaluation, create a .env configuration file in your working directory that defines the INSPECT_EVAL_MODEL environment variable along with your API key. For example:

INSPECT_EVAL_MODEL=anthropic/claude-3-5-sonnet-20240620
ANTHROPIC_API_KEY=<anthropic-api-key>

Options

You can control a variety of options from the command line. For example:

inspect eval inspect_evals/mmlu --limit 10
inspect eval inspect_evals/mmlu --max-connections 10
inspect eval inspect_evals/mmlu --temperature 0.5

See inspect eval --help for all available options.

Dataset

Here is an example prompt from the dataset (after it has been further processed by Inspect):

Answer the following multiple choice question. The entire content of your response should be of the following format: ‘ANSWER: $LETTER’ (without quotes) where LETTER is one of A,B,C,D.

The constellation … is a bright W-shaped constellation in the northern sky.

  1. Centaurus
  2. Cygnus
  3. Cassiopeia
  4. Cepheus

The model is then tasked to pick the correct choice.

Scoring

A simple accuracy is calculated over the datapoints.