From Training to Generalization: Improving Moral Reasoning Through Pragmatic Inference

Abstract

Although moral reasoning has emerged as a promising research direction for large language models (LLMs), a persistent generalization challenge remains: LLMs often achieve strong performance on training data but struggle to generalize their moral reasoning to unseen test data. From a linguistic perspective, moral reasoning is a pragmatic process in which moral judgments are inferred based on the context of social norms underlying a given moral situation. However, existing approaches overlook this pragmatic nature because of two major bottlenecks: (1) LLMs are primarily skilled in capturing distributional semantics, which differs from the pragmatic nature of moral reasoning; (2) there is currently no effective solution for grounding language in the moral context. In this paper, we develop a pragmatic inference approach that enables LLMs to infer moral judgments for a given moral situation by combining metapragmatic links with Moral Foundations Theory. Specifically, metapragmatic links serve to bridge the gap between distributional semantics and pragmatics, whereas Moral Foundations Theory provides a principled basis for grounding language in moral contexts. Experimental results demonstrate that our approach substantially improves LLMs' generalization in moral reasoning, highlighting the potential of pragmatic inference for future moral reasoning research.