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arXiv:2109.00110cs.AI

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

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.

arXiv comments
Published as a conference paper at ICLR 2022
Journal reference
DOI
Subjects
cs.AIArtificial Intelligence cs.FLFormal Languages and Automata Theory cs.LGMachine Learning

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  1. GPT-4 1 comment

    • > It's AMC-12 scores aren't awful. A blank test scores 37.5 The best score 60 is 5 correct answers + 20 blank answers; or 6 correct, 4 correct random guesses, and 15 incorrect random guesses. (20% chance of correct guess) The 5 easiest questions are relatively simple…

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