The formalization of existing mathematical proofs is a notoriously difficult process. Despite decades of research on automation and proof assistants, writing formal proofs remains arduous and only accessible to a few experts. While previous studies to automate formalization focused on powerful search algorithms, no attempts were made to take advantage of available informal proofs. In this work, we introduce Draft, Sketch, and Prove (DSP), a method that maps informal proofs to formal proof sketches, and uses the sketches to guide an automated prover by directing its search to easier sub-problems. We investigate two relevant setups where informal proofs are either written by humans or generated by a language model. Our experiments and ablation studies show that large language models are able to produce well-structured formal sketches that follow the same reasoning steps as the informal proofs. Guiding an automated prover with these sketches enhances its performance from 20.9% to 39.3% on a collection of mathematical competition problems.
翻译:现有数学证明的正规化是一个臭名昭著的困难过程。 尽管对自动化和证据助理进行了数十年的研究,但编写正式证明仍然是艰巨的,而且只有少数专家可以查阅。 虽然以前关于自动化正规化的研究侧重于强大的搜索算法,但没有尝试利用现有的非正式证明。 在这项工作中,我们引入了草稿、 Sletch 和Prove (DSP ), 这是一种绘制非正式证明以正式证明草图的方法, 并使用草图指导自动验证师, 将搜索方向转向较简单的次级问题。 我们调查了两个相关的设置, 即非正式证明要么由人编写,要么由语言模型生成。 我们的实验和修正研究表明, 大语言模型能够产生结构完善的正式草图, 遵循与非正式证明相同的推理步骤。 指导一个自动验证师使用这些草图,可以提高它收集数学竞争问题的性能,从20.9%到39.3%。