Autoformalization with Large Language Models

Autoformalization is the process of automatically translating from natural language mathematics to formal specifications and proofs. A successful autoformalization system could advance the fields of formal verification, program synthesis, and artificial intelligence. While the long-term goal of autoformalization seemed elusive for a long time, we show large language models provide new prospects towards this goal. We make the surprising observation that LLMs can correctly translate a significant portion ($25. 3\%$) of mathematical competition problems perfectly to formal specifications in Isabelle/HOL. We demonstrate the usefulness of this process by improving a previously introduced neural theorem prover via training on these autoformalized theorems. Our methodology results in a new state-of-the-art result on the MiniF2F theorem proving benchmark, improving the proof rate from~$29. 6\%$ to~$35. 2\%$.

Authors

• Yuhuai Wu
• Albert Qiaochu Jiang
• Wenda Li
• Markus Rabe
• Charles Staats
• Mateja Jamnik University of Cambridge
• Christian Szegedy

Keywords

No keywords are indexed for this paper.