  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 $35.2\%$.
