MiniF2F: a cross-system benchmark for formal Olympiad-level mathematics
This provides a unified benchmark for researchers in neural theorem proving, though it is incremental as it builds on existing datasets and methods.
The authors introduced miniF2F, a dataset of 488 formal Olympiad-level mathematics problems from sources like AIME, AMC, and IMO, to serve as a cross-system benchmark for neural theorem proving, and reported baseline results using GPT-f, a neural theorem prover based on GPT-3.
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.