Enhancing Neural Theorem Proving through Data Augmentation and Dynamic Sampling Method

arXiv:2312.14188v2 Announce Type: replace-cross
Abstract: Theorem proving is a fundamental task in mathematics. With the advent of large language models (LLMs) and interactive theorem provers (ITPs) like Lean, there has been growing interest in integrating LLMs and ITPs to automate theorem proving. In this approach, the LLM generates proof steps (tactics), and the ITP checks the applicability of the tactics at the current goal. The two systems work together to complete the proof. In this paper, we introduce DS-Prover, a …

abstract arxiv augmentation automate cs.ai cs.lg cs.lo data dynamic interactive language language models large language large language models lean llm llms mathematics sampling theorem through type