Moral reasoning has emerged as a promising research direction for Large Language Models (LLMs), yet achieving generalization remains a central challenge. From a linguistic standpoint, this difficulty arises because LLMs are adept at capturing distributional semantics, which fundamentally differs from the morals which operate at the pragmatic level. This paper investigates how LLMs can achieve generalized moral reasoning despite their reliance on distributional semantics. We propose pragmatic inference methods grounded in moral foundations theory, which leverage contextual information at each step to bridge the pragmatic gap and guide LLMs in connecting moral foundations with moral reasoning objectives. Experimental results demonstrate that our approach significantly enhances LLMs' generalization in moral reasoning, providing a foundation for future research grounded in moral foundations theory.

翻译：道德推理已成为大型语言模型（LLMs）的一个重要研究方向，然而实现泛化仍是核心挑战。从语言学角度看，这一困难源于LLMs擅长捕捉分布语义，而道德运作本质上处于语用层面。本文探究LLMs如何在依赖分布语义的前提下实现泛化的道德推理。我们基于道德基础理论提出语用推断方法，通过逐步骤利用上下文信息弥合语用鸿沟，引导LLMs将道德基础与道德推理目标相连接。实验结果表明，该方法显著提升了LLMs在道德推理中的泛化能力，为未来基于道德基础理论的研究奠定了基础。