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Computer Science > Artificial Intelligence

arXiv:2109.00110 (cs)
[Submitted on 31 Aug 2021 (v1), last revised 28 Feb 2022 (this version, v2)]

Title:MiniF2F: a cross-system benchmark for formal Olympiad-level mathematics

Authors:Kunhao Zheng, Jesse Michael Han, Stanislas Polu
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Abstract:We present miniF2F, a dataset of formal Olympiad-level mathematics problems statements intended to provide a unified cross-system benchmark for neural theorem proving. The miniF2F benchmark currently targets Metamath, Lean, Isabelle (partially) and HOL Light (partially) and consists of 488 problem statements drawn from the AIME, AMC, and the International Mathematical Olympiad (IMO), as well as material from high-school and undergraduate mathematics courses. We report baseline results using GPT-f, a neural theorem prover based on GPT-3 and provide an analysis of its performance. We intend for miniF2F to be a community-driven effort and hope that our benchmark will help spur advances in neural theorem proving.
Comments: Published as a conference paper at ICLR 2022
Subjects: Artificial Intelligence (cs.AI); Formal Languages and Automata Theory (cs.FL); Machine Learning (cs.LG)
Cite as: arXiv:2109.00110 [cs.AI]
  (or arXiv:2109.00110v2 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2109.00110

Submission history

From: Kunhao Zheng [view email]
[v1] Tue, 31 Aug 2021 23:21:12 UTC (82 KB)
[v2] Mon, 28 Feb 2022 06:03:23 UTC (73 KB)
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