Are We Done with MMLU?

Aryo Pradipta Gema, Joshua Ong Jun Leang, Giwon Hong, Alessio, Alberto Carlo Maria Mancino, Rohit Saxena, Xuanli He, Yu Zhao, Xiaotang Du, Mohammad Reza Ghasemi Madani, Claire Barale, Robert McHardy , Harris, Jean Kaddour, Emile van Krieken, Pasquale Minervini

Are We Done with MMLU?: 35 upvotes on Hugging Face Daily Papers, #4 of 21 papers on 2024-06-13. Day-by-day upvote history. It lost 5 votes when the Hub removed votes in bulk.

Maybe not. We identify and analyse errors in the popular Massive Multitask Language Understanding (MMLU) benchmark. Even though MMLU is widely adopted, our analysis demonstrates numerous ground truth errors that obscure the true capabilities of LLMs. For example, we find that 57% of the analysed questions in the Virology subset contain errors. To address this issue, we introduce a comprehensive framework for identifying dataset errors using a novel error taxonomy. Then, we create MMLU-Redux, which is a subset of 3,000 manually re-annotated questions across 30 MMLU subjects. Using MMLU-Redux, we demonstrate significant discrepancies with the model performance metrics that were originally reported. Our results strongly advocate for revising MMLU's error-ridden questions to enhance its future utility and reliability as a benchmark. Therefore, we open up MMLU-Redux for additional annotation https://huggingface.co/datasets/edinburgh-dawg/mmlu-redux.

Paper page on Hugging Face · arXiv

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