Humanity's Last Exam
Long Phan∗1 , Alice Gatti∗1 , Ziwen Han∗2 , Nathaniel Li∗1 , Josephina Hu2 , Hugh Zhang‡, Sean Shi2, Michael Choi2, Anish Agrawal2, Arnav Chopra2, Adam Khoja1, Ryan Kim†, Richard Ren1, Jason Hausenloy1, Oliver Zhang1 , Mantas Mazeika1 , Summer Yue∗∗2 , Alexandr Wang∗∗2 , Dan Hendrycks∗∗1
1 Center for AI Safety, 2 Scale AI
∗Co-first Authors. ∗∗ Senior Authors. † Work conducted while at Center for AI Safety. ‡ Work conducted while at Scale AI.
Refer to PDF for full list of Dataset Contributors.
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce HUMANITY’S LAST EXAM (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 3,000 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai.
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