title: Language Models can be Logical Solvers
summary: Logical reasoning is a fundamental aspect of human intelligence and a key
  component of tasks like problem-solving and decision-making. Recent
  advancements have enabled Large Language Models (LLMs) to potentially exhibit
  reasoning capabilities, but complex logical reasoning remains a challenge. The
  state-of-the-art, solver-augmented language models, use LLMs to parse natural
  language logical questions into symbolic representations first and then adopt
  external logical solvers to take in the symbolic representations and output the
  answers. Despite their impressive performance, any parsing errors will
  inevitably result in the failure of the execution of the external logical
  solver and no answer to the logical questions. In this paper, we introduce
  LoGiPT, a novel language model that directly emulates the reasoning processes
  of logical solvers and bypasses the parsing errors by learning to strict
  adherence to solver syntax and grammar. LoGiPT is fine-tuned on a newly
  constructed instruction-tuning dataset derived from revealing and refining the
  invisible reasoning process of deductive solvers. Experimental results on two
  public deductive reasoning datasets demonstrate that LoGiPT outperforms
  state-of-the-art solver-augmented LMs and few-shot prompting methods on
  competitive LLMs like ChatGPT or GPT-4.
paper_authors._id: 6551911a0c11ee1eb9ff5807
paper_authors.name: Jiazhan Feng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-13T11:01:06.873Z
paper_authors._id: 649a95f951e1ea30f1475530
paper_authors.avatarUrl: /avatars/d4d167c3985e702044e8290408dde661.svg
paper_authors.fullname: Jamie Jiazhan Feng
paper_authors.type: user
paper_authors.user: jzfeng
paper_authors._id: 6551911a0c11ee1eb9ff5808
paper_authors.name: Ruochen Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-13T11:01:14.926Z
paper_authors._id: 64a234a8d94c3f9ccec8476d
paper_authors.avatarUrl: /avatars/633adf359b1a65f458c3b5a7e59f11c0.svg
paper_authors.fullname: Ruochen Xu
paper_authors.type: user
paper_authors.user: ruochenx
paper_authors._id: 6551911a0c11ee1eb9ff5809
paper_authors.name: Junheng Hao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-13T11:01:21.904Z
paper_authors._id: 5f04c4394ec31d33a72116d6
paper_authors.avatarUrl: /avatars/75d4b9020070e73604b12e5adc1c8201.svg
paper_authors.fullname: Junheng Hao
paper_authors.type: user
paper_authors.user: jeffhao
paper_authors._id: 6551911a0c11ee1eb9ff580a
paper_authors.name: Hiteshi Sharma
paper_authors._id: 6551911a0c11ee1eb9ff580b
paper_authors.name: Yelong Shen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-13T11:02:06.617Z
paper_authors._id: 6454c337a13edf669cd5d8ea
paper_authors.avatarUrl: /avatars/a383a0dda7c2ef6a0d6c3c64651f42ff.svg
paper_authors.fullname: Yelong Shen
paper_authors.type: user
paper_authors.user: uuu6
paper_authors._id: 6551911a0c11ee1eb9ff580c
paper_authors.name: Dongyan Zhao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-13T11:02:47.258Z
paper_authors._id: 629ee8171fec321a2d3c458d
paper_authors.avatarUrl: /avatars/a8ae78b193ba2a43c6e36e185e0ef2b9.svg
paper_authors.fullname: Zhaodong Yan
paper_authors.type: user
paper_authors.user: Anhforth
paper_authors._id: 6551911a0c11ee1eb9ff580d
paper_authors.name: Weizhu Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-13T11:02:54.267Z
paper_authors._id: 64da876370446182be5b608d
paper_authors.avatarUrl: /avatars/e412fdc71404ecdf638e416846e3ebfb.svg
paper_authors.fullname: Weizhu Chen
paper_authors.type: user
paper_authors.user: chenweizhu
paper_title: Language Models can be Logical Solvers
paper_summary: Logical reasoning is a fundamental aspect of human intelligence and a key
  component of tasks like problem-solving and decision-making. Recent
  advancements have enabled Large Language Models (LLMs) to potentially exhibit
  reasoning capabilities, but complex logical reasoning remains a challenge. The
  state-of-the-art, solver-augmented language models, use LLMs to parse natural
  language logical questions into symbolic representations first and then adopt
  external logical solvers to take in the symbolic representations and output the
  answers. Despite their impressive performance, any parsing errors will
  inevitably result in the failure of the execution of the external logical
  solver and no answer to the logical questions. In this paper, we introduce
  LoGiPT, a novel language model that directly emulates the reasoning processes
  of logical solvers and bypasses the parsing errors by learning to strict
  adherence to solver syntax and grammar. LoGiPT is fine-tuned on a newly
  constructed instruction-tuning dataset derived from revealing and refining the
  invisible reasoning process of deductive solvers. Experimental results on two
  public deductive reasoning datasets demonstrate that LoGiPT outperforms
  state-of-the-art solver-augmented LMs and few-shot prompting methods on
  competitive LLMs like ChatGPT or GPT-4.
paper_ai_summary: LoGiPT, a new language model, directly emulates logical solver reasoning processes and outperforms state-of-the-art solver-augmented LMs and few-shot prompting methods on deductive reasoning tasks.
paper_ai_keywords: Large Language Models LLMs logical reasoning symbolic representations logical solvers LoGiPT deductive solvers instruction-tuning few-shot prompting ChatGPT GPT-4