Language serves as a vehicle for conveying thought, enabling communication among individuals. The ability to distinguish between diverse concepts, identify fairness and injustice, and comprehend a range of legal notions fundamentally relies on logical reasoning. Large Language Models (LLMs) attempt to emulate human language understanding and generation, but their competency in logical reasoning remains limited. This paper seeks to address the philosophical question: How can we effectively teach logical reasoning to LLMs while maintaining a deep understanding of the intricate relationship between language and logic? By focusing on bolstering LLMs' capabilities in logical reasoning, we aim to expand their applicability in law and other logic-intensive disciplines. To this end, we propose a Reinforcement Learning from Logical Feedback (RLLF) approach, which serves as a potential framework for refining LLMs' reasoning capacities. Through RLLF and a revised evaluation methodology, we explore new avenues for research in this domain and contribute to the development of LLMs capable of handling complex legal reasoning tasks while acknowledging the fundamental connection between language and logic.

翻译：语言作为传达思想的载体，使个体之间能够进行交流。区分不同概念、识别公平与不公、理解一系列法律概念的能力，根本上依赖于逻辑推理。大型语言模型（LLMs）试图模仿人类语言理解与生成能力，但其逻辑推理能力仍然有限。本文旨在探讨一个哲学问题：如何在保持对语言与逻辑之间复杂关系深刻理解的同时，有效教授LLMs逻辑推理？通过聚焦于增强LLMs的逻辑推理能力，我们旨在拓展其在法律及其他逻辑密集型学科中的适用性。为此，我们提出一种基于逻辑反馈的强化学习方法（RLLF），作为优化LLMs推理能力的潜在框架。通过RLLF及修订的评估方法，我们探索了该领域研究的新方向，并推动了能够处理复杂法律推理任务且认知语言与逻辑基本关联的LLMs的发展。