MiniF2F: a cross-system benchmark for formal Olympiad-level mathematics
arXiv:2109.00110
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.
Published as a conference paper at ICLR 2022
References in corpus (6)
- Language Models are Few-Shot Learners
- The Lean mathematical library
- Analysing Mathematical Reasoning Abilities of Neural Models
- Generative Language Modeling for Automated Theorem Proving
- Holophrasm: a neural Automated Theorem Prover for higher-order logic
- Learning to Prove Theorems via Interacting with Proof Assistants