
























Abstract: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.
From: Guangliang Liu [view email]
[v1]
Sun, 28 Sep 2025 22:40:58 UTC (146 KB)
[v2]
Mon, 15 Dec 2025 21:00:36 UTC (453 KB)
[v3]
Tue, 13 Jan 2026 20:57:38 UTC (453 KB)
[v4]
Sun, 15 Feb 2026 13:22:22 UTC (487 KB)
[v5]
Thu, 11 Jun 2026 18:21:55 UTC (514 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。