Papers › Generative Language Modeling for Automated Theorem Proving
Generative Language Modeling for Automated Theorem Proving
Stanislas Polu, Ilya Sutskever
We explore the application of transformer-based language models to automated theorem proving. This work is motivated by the possibility that a major limitation of automated theorem provers compared to humans -- the generation of original mathematical terms -- might be addressable via generation from language models. We present an automated prover and proof assistant, GPT-f, for the Metamath formalization language, and analyze its performance. GPT-f found new short proofs that were accepted into the main Metamath library, which is to our knowledge, the first time a deep-learning based system has contributed proofs that were adopted by a formal mathematics community.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Automated Theorem Proving | Metamath set.mm | GPT-f | Percentage correct | 56.2 | #1 of 4 | Archive leaderboard | report |
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