miniF2F: a cross-system benchmark for formal Olympiad-level mathematics
Kunhao Zheng, Jesse Michael Han, Stanislas Polu
Abstract
We present $\textsf{miniF2F}$, a dataset of formal Olympiad-level mathematics problems statements intended to provide a unified cross-system benchmark for neural theorem proving. The $\textsf{miniF2F}$ 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 $\textsf{miniF2F}$ to be a community-driven effort and hope that our benchmark will help spur advances in neural theorem proving.
BibTeX
@inproceedings{
zheng2022miniff,
title={miniF2F: a cross-system benchmark for formal Olympiad-level mathematics},
author={Kunhao Zheng and Jesse Michael Han and Stanislas Polu},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=9ZPegFuFTFv}
}