MiniF2F: a cross-system benchmark for formal Olympiad-level mathematics
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.
PDF Abstract ICLR 2022 PDF ICLR 2022 Abstract
Datasets
MiniF2F
MATH
NaturalProofs
Results from the Paper
Ranked #1 on
Automated Theorem Proving
on miniF2F-valid
(using extra training data)
