语言模型可以成为逻辑求解器
Language Models can be Logical Solvers
AI总结：
本文提出LoGiPT，一种直接模拟逻辑求解器推理过程并严格遵守其语法的新语言模型，通过新构建的指令调优数据集微调，在两个公开演绎推理数据集上优于现有求解器增强模型和少样本提示方法。
摘要：
逻辑推理是人类智能的基本方面，也是解决问题和决策等任务的关键组成部分。最近的进展使得大型语言模型（LLMs）有可能展现出推理能力，但复杂的逻辑推理仍然是一个挑战。最先进的、由求解器增强的语言模型，首先使用LLMs将自然语言逻辑问题解析为符号表示，然后采用外部逻辑求解器接收符号表示并输出答案。尽管其性能令人印象深刻，但任何解析错误都不可避免地导致外部逻辑求解器执行失败，从而无法回答逻辑问题。在本文中，我们介绍了LoGiPT，一种新颖的语言模型，它直接模拟逻辑求解器的推理过程，并通过学习严格遵守求解器语法和文法来绕过解析错误。LoGiPT在一个新构建的指令调优数据集上进行微调，该数据集来源于揭示和提炼演绎求解器的不可见推理过程。在两个公开的演绎推理数据集上的实验结果表明，LoGiPT优于最先进的求解器增强语言模型以及在ChatGPT或GPT-4等竞争性LLM上的少样本提示方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
