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╚═════╝ ╚═╝ ╚══▀▀═╝ ╚═╝ ╚═╝$ open /benchmarks/gpqa▌ready
GPQA
Science
GPQA is a set of graduate-level questions in biology, physics and chemistry written so that non-experts with web access still fail them. The Diamond subset is the most-used slice.
gpqa
History
David Rein and collaborators introduced GPQA in 2023 to test expert scientific knowledge that could not readily be substituted by web search. The benchmark focuses on graduate-level biology, physics and chemistry. Its motivation was to construct questions that domain experts could answer but skilled non-experts would struggle with even when allowed to search online.
How it was built
Domain experts wrote the multiple-choice questions, and the collection process included expert validation and attempts by non-experts with web access. This design checks both specialist answerability and resistance to straightforward search. The Diamond subset selects questions using the validation results and is a distinct evaluation slice rather than another name for the whole dataset.
At a glance
- Size
- Not imported here; see the official distribution for the current size.
- License
- cc-by-4.0 (declared on the source)
- Released
- Dataset published on Hugging Face 2023-11
- Maintainer
- Idavidrein (source organisation)
- Answer access
- Access is gated and the authors ask that the questions not be propagated in plain text, to keep them out of training data. This catalogue links to the official distribution instead.
What a problem looks like
Graduate-level multiple-choice questions in biology, physics and chemistry.
How it is scored
Access is gated and the authors ask that the questions not be propagated in plain text, to keep them out of training data. This catalogue links to the official distribution instead.
Why it is hard
Written and validated so that skilled non-experts with unrestricted web access still score near chance.
Limitations and criticism
GPQA measures selection among supplied answers in three scientific fields, rather than experimental ability or the production of independently verified research. Results on Diamond should not be conflated with results on other subsets. The authors restrict access and ask that questions not be propagated as plain text to reduce training contamination; those precautions do not establish that any particular model has never encountered the material.
Reference links
- Issues
- Paper
- Dataset
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Discussion
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Share a reproduction, report an evaluation issue, or suggest a correction to this benchmark overview.
Public problem summary
Catalogue metadata only; public problem statistics are not available.
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