Logic Agent：通过逻辑规则调用增强有效性
Logic Agent: Enhancing Validity with Logic Rule Invocation
AI总结：
本文提出 Logic Agent 框架，通过将自然语言转为逻辑形式并动态调用命题逻辑规则，提升 LLM 推理的有效性、可解释性与跨规模任务表现。
摘要：
Chain-of-Thought（CoT，思维链）提示已成为在推理任务中增强语言模型推理能力的一项关键技术。尽管取得了进展，CoT 仍常常难以验证推理的有效性并确保信息性。针对这些局限，本文提出 Logic Agent（LA），这是一个基于智能体的框架，旨在通过策略性的逻辑规则调用来增强 Large Language Models（LLMs，大语言模型）推理过程的有效性。与传统方法不同，LA 将 LLM 转化为逻辑智能体，使其动态应用命题逻辑规则，并通过将自然语言输入转换为结构化逻辑形式来启动推理过程。该逻辑智能体利用一整套预定义函数来系统地引导推理过程。该方法不仅促进了推理结构的结构化、连贯生成，还显著提升了其可解释性和逻辑一致性。通过大量实验，我们证明 LA 能够在不同模型规模上有效扩展，并在多种任务中显著提高复杂推理的精确性。
英文摘要：
Chain-of-Thought (CoT) prompting has emerged as a pivotal technique for augmenting the inferential capabilities of language models during reasoning tasks. Despite its advancements, CoT often grapples with challenges in validating reasoning validity and ensuring informativeness. Addressing these limitations, this paper introduces the Logic Agent (LA), an agent-based framework aimed at enhancing the validity of reasoning processes in Large Language Models (LLMs) through strategic logic rule invocation. Unlike conventional approaches, LA transforms LLMs into logic agents that dynamically apply propositional logic rules, initiating the reasoning process by converting natural language inputs into structured logic forms. The logic agent leverages a comprehensive set of predefined functions to systematically navigate the reasoning process. This methodology not only promotes the structured and coherent generation of reasoning constructs but also significantly improves their interpretability and logical coherence. Through extensive experimentation, we demonstrate LA's capacity to scale effectively across various model sizes, markedly improving the precision of complex reasoning across diverse tasks.
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。
