MMLU-Pro

|🤗 Dataset | 🏆Leaderboard | 📖 Paper |

This repo contains the evaluation code for the paper "MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark"

Introduction

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.

Dataset Creation

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.

Evaluation

To run local inference, modify the model name in the following script and execute it:

cd scripts/examples/
sh eval_llama_2_7b.sh

To 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

🏆 Mini-Leaderboard

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.

Benchmarking Answer Extraction

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.

Contact

Citation

BibTeX:

@misc{wang2024mmlupro,
      title={MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark}, 
      author={Yubo Wang and Xueguang Ma and Ge Zhang and Yuansheng Ni and Abhranil Chandra and Shiguang Guo and Weiming Ren and Aaran Arulraj and Xuan He and Ziyan Jiang and Tianle Li and Max Ku and Kai Wang and Alex Zhuang and Rongqi Fan and Xiang Yue and Wenhu Chen},
      year={2024},
      eprint={2406.01574},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

netapp-rickhuang-ai/MMLU-Pro-eval-JupyterHub-MLflow-DOTK