|🤗 Dataset | 🏆Leaderboard | 📖 Paper |
This repo contains the evaluation code for the NeurIPS-24 paper "MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark"
We introduce MMLU-Pro, an enhanced benchmark designed to evaluate language understanding models across broader and more challenging tasks. Building on the Massive Multitask Language Understanding (MMLU) dataset, MMLU-Pro integrates more challenging, reasoning-focused questions and increases the answer choices per question from four to ten, significantly raising the difficulty and reducing the chance of success through random guessing. MMLU-Pro comprises over 12,000 rigorously curated questions from academic exams and textbooks, spanning 14 diverse domains including Biology, Business, Chemistry, Computer Science, Economics, Engineering, Health, History, Law, Math, Philosophy, Physics, Psychology, and Others.
Our experimental results show that MMLU-Pro not only raises the challenge, causing a significant drop in accuracy by 16% to 33% compared to MMLU but also demonstrates greater stability under varying prompts. With 24 different prompt styles tested, the sensitivity of model scores to prompt variations decreased from 4-5% in MMLU to just 2% in MMLU-Pro. Additionally, we found that models utilizing Chain of Thought (CoT) reasoning achieved better performance on MMLU-Pro compared to direct answering, which starkly contrasts the findings on the original MMLU, indicating that MMLU-Pro includes more complex reasoning questions.
October 10, 2024: Added the 24 tested prompt styles from the paper to the repository.
MMLU-Pro was created to provide language models with a more challenging and robust benchmark, pushing the boundaries of what these models can achieve in terms of expert-level knowledge and reasoning. Please refer to our huggingface 🤗 Dataset for more details.
To run local inference, modify the model name in the following script and execute it:
cd scripts/examples/
sh eval_llama_2_7b.shTo use the API for inference, modify the API KEY in evaluate_from_api.py script and execute the bash script:
cd scripts/examples/
sh eval_gpt_4.sh| Model | Overall Accuracy |
|---|---|
| Claude-3.5-Sonnet | 76.12 |
| GPT-4o | 72.55 |
| Gemini-1.5-Pro | 69.03 |
| Claude-3-Opus | 68.45 |
| GPT-4-Turbo | 63.71 |
| Gemini-1.5-Flash | 59.12 |
| Yi-large | 57.53 |
| Claude-3-Sonnet | 56.80 |
| Llama-3-70B-Instruct | 56.20 |
| Phi3-medium-4k | 55.70 |
| Deepseek-V2-Chat | 54.81 |
| Phi-3-medium-4k-instruct | 53.48 |
| Llama-3-70B | 52.78 |
| Qwen1.5-72B-Chat | 52.64 |
| Yi-1.5-34B-Chat | 52.29 |
| Phi3-medium-128k | 51.91 |
| MAmmoTH2-8x7B-Plus | 50.40 |
For more details on various models and their accuracy across different subjects, please visit our Leaderboard.
We provide different alternatives to do answer extraction. We found that different answer extraction mechanisms have minor impact on the results.
python compute_accuracy.py results/llama-3-8b-quantized/CoT/all/
Thanks to @chibop1 for evaluating the robustness of MMLU-Pro across all the different answer extraction strategies and temperature. A detailed discussion is posted at Reddit.
- Yubo Wang: y726wang@uwaterloo.ca
- Xueguang Ma: x93ma@uwaterloo.ca
- Wenhu Chen: wenhuchen@uwaterloo.ca
BibTeX:
@article{wang2024mmlu,
title={Mmlu-pro: A more robust and challenging multi-task language understanding benchmark},
author={Wang, Yubo and Ma, Xueguang and Zhang, Ge and Ni, Yuansheng and Chandra, Abhranil and Guo, Shiguang and Ren, Weiming and Arulraj, Aaran and He, Xuan and Jiang, Ziyan and others},
journal={arXiv preprint arXiv:2406.01574},
year={2024}
}
