A Survey on Deep Learning for Theorem Proving
Published in Conference on Language Modeling (COLM), 2024
We presents a pioneering comprehensive survey of deep learning for theorem proving by offering i) a thorough review of existing approaches across various tasks such as autoformalization, premise selection, proofstep generation, and proof search; ii) a meticulous summary of available datasets and strategies for data generation; iii) a detailed analysis of evaluation metrics and the performance of state-of-the-art; and iv) a critical discussion on the persistent challenges and the promising avenues for future exploration.
Recommended citation: Zhaoyu Li, Jialiang Sun, Logan Murphy, Qidong Su, Zenan Li, Xian Zhang, Kaiyu Yang, and Xujie Si. A Survey on Deep Learning for Theorem Proving. In proceedings of the Conference on Language Modeling, 2024.
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