LeanDojo: Theorem Proving with Retrieval-Augmented Language Models
Under review, NeurIPS (Datasets and Benchmarks Track), 2023
Kaiyu Yang, Aidan Swope, Alex Gu, Rahul Chalamala,
Peiyang Song, Shixing Yu, Saad Godil, Ryan Prenger, Anima Anandkumar
@inproceedings{yang2023leandojo,
title={{LeanDojo}: Theorem Proving with Retrieval-Augmented Language Models},
author={Yang, Kaiyu and Swope, Aidan and Gu, Alex and Chalamala, Rahul and Song, Peiyang and Yu, Shixing and Godil, Saad and Prenger, Ryan and Anandkumar, Anima},
booktitle={Neural Information Processing Systems (NeurIPS)},
year={2023}
}
Please visit LeanDojo Website for details.
Input Format
The model's input consists of retrieved premises concatenated with the current proof state and truncated to 2300 UTF-8 bytes.
The proof state is formatted by Lean's pretty printer, the same as the input format of our model w/o retrieval.
Retrieved premises are in the form of Lean code, except that proofs are removed and fully qualified names (marked by <a>...</a>) are used. Please see the example on the right.