MMLU Pro

MMLU Pro is a critical benchmark for evaluating Large Language Models (LLMs). It assesses an LLM's general knowledge and understanding across 57+ diverse subjects, providing a quantitative method to compare and select models for specific applications and guide development.

beginner15 min5 steps

The play

Understand MMLU Pro's Role
Grasp that MMLU Pro is a standardized benchmark designed to measure an LLM's factual and conceptual understanding across a broad spectrum of academic and professional subjects (57+ domains).
Access MMLU-Pro Data
Locate and explore the MMLU-Pro dataset, often available on platforms like Hugging Face, to understand its structure and content for evaluation purposes.
Interpret Benchmark Results
Analyze published MMLU-Pro scores for various LLMs to identify their general intelligence, strengths, and weaknesses across different knowledge domains.
Apply Insights for LLM Selection
Utilize MMLU-Pro performance metrics to make informed decisions when selecting an LLM for a specific application, moving beyond anecdotal evidence to data-driven choices.
Inform LLM Improvement Strategies
Leverage MMLU-Pro insights to guide fine-tuning efforts, refine prompt engineering strategies, or suggest architectural improvements for your LLM, focusing on areas where it underperforms.

Starter code

from datasets import load_dataset

# Load the MMLU-Pro dataset
dataset = load_dataset("TIGER-Lab/MMLU-Pro")

# Print the dataset structure to understand its splits and features
print(dataset)

Source

Articlehuggingface.co