Humanity's Last Exam
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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2025-05-09T07:23:15.016Z
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2025-04-29T07:59:19.472Z
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2025-02-26T08:42:57.161Z
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2025-02-03T08:16:10.413Z
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2025-04-09T16:22:51.134Z
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2025-05-29T07:47:14.228Z
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Vasilios Mavroudis
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2025-02-03T08:16:07.872Z
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Vasilios Mavroudis
user
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Eric Vergo
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Juehang Qin
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Benjámin Borbás
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Eric Chu
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Jack Lindsey
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Anil Radhakrishnan
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Antoine Jallon
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I. M. J. McInnis
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Alex Hoover
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Sören Möller
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Song Bian
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John Lai
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Tejal Patwardhan
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Summer Yue
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Alexandr Wang
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Dan Hendrycks
Humanity's Last Exam
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.
HLE is a challenging multi-modal benchmark that highlights the limitations of current LLMs in closed-ended academic questions.
large language model (LLM) benchmarks MMLU Humanity's Last Exam (HLE) multi-modal benchmark broad subject coverage multiple-choice short-answer questions automated grading accuracy