title: Large Language Model for Science: A Study on P vs. NP
summary: 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.
paper_authors._id: 65010a115d76377562ceeac0
paper_authors.name: Qingxiu Dong
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paper_authors.fullname: Qingxiu Dong
paper_authors.type: user
paper_authors.user: Rsy24
paper_authors._id: 65010a115d76377562ceeac1
paper_authors.name: Li Dong
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paper_authors.fullname: Li Dong
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paper_authors.user: unilm
paper_authors._id: 65010a115d76377562ceeac2
paper_authors.name: Ke Xu
paper_authors._id: 65010a115d76377562ceeac3
paper_authors.name: Guangyan Zhou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-13T13:22:25.070Z
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paper_authors.fullname: Guangyan Zhou
paper_authors.type: user
paper_authors.user: Chiken1
paper_authors._id: 65010a115d76377562ceeac4
paper_authors.name: Yaru Hao
paper_authors._id: 65010a115d76377562ceeac5
paper_authors.name: Zhifang Sui
paper_authors._id: 65010a115d76377562ceeac6
paper_authors.name: Furu Wei
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-13T13:23:18.350Z
paper_authors._id: 6368c512fbfe97c16a40baba
paper_authors.avatarUrl: /avatars/1c23bc7c0b6d9225699ce27647623d7a.svg
paper_authors.fullname: Furu Wei
paper_authors.type: user
paper_authors.user: thegenerality
paper_title: Large Language Model for Science: A Study on P vs. NP
paper_summary: 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.
paper_ai_keywords: large language models (LLMs) Socratic reasoning recursive discovery problem-solving self-evaluation refinement proof schema rigorous reasoning GPT-4 dialogue turns solution space LLM for Science