While moral reasoning has emerged as a promising research direction for large language models (LLMs), achieving robust generalization remains a critical challenge. This challenge arises from the gap between what is said and what is morally implied. In this paper, we build on metapragmatic links and Moral Foundations Theory to close this gap. Specifically, we develop a pragmatic inference approach that enables LLMs, given a moral situation, to acquire the metapragmatic links between moral reasoning objectives and the social variables that influence them. We adapt this approach to three different moral reasoning tasks to demonstrate its adaptability and generalizability. Experimental results show that our approach significantly enhances LLMs' generalization in moral reasoning, paving the way for future research to leverage pragmatic inference across a wide range of moral reasoning tasks.

翻译：尽管道德推理已成为大型语言模型（LLMs）富有前景的研究方向，但实现稳健泛化仍是重大挑战。这一挑战源于字面表述与道德隐含意义之间的鸿沟。本文基于元语用关联与道德基础理论弥合这一鸿沟，具体开发了一种语用推断方法，使LLMs能在给定道德情境下，习得道德推理目标与其影响因素之间的元语用关联。我们将该方法适配至三种不同的道德推理任务以验证其适应性与泛化能力。实验结果表明，该方法显著提升了LLMs在道德推理中的泛化性能，为未来研究利用语用推断处理各类道德推理任务开辟了道路。