Large Language Model for Science: A Study on P vs. NP
Qingxiu Dong Li Dong Ke Xu Guangyan Zhou Yaru Hao Zhifang Sui Furu Wei
In this work, we use large language models (LLMs) to augment and accelerate
research on the P versus NP problem, one of the most important open problems in
theoretical computer science and mathematics. Specifically, we propose Socratic
reasoning, a general framework that promotes in-depth thinking with LLMs for
complex problem-solving. Socratic reasoning encourages LLMs to recursively
discover, solve, and integrate problems while facilitating self-evaluation and
refinement. Our pilot study on the P vs. NP problem shows that GPT-4
successfully produces a proof schema and engages in rigorous reasoning
throughout 97 dialogue turns, concluding "P $\neq$ NP", which is in alignment
with (Xu and Zhou, 2023). The investigation uncovers novel insights within the
extensive solution space of LLMs, shedding light on LLM for Science.