Learning to Diagnose and Correct Errors: Towards Moral Sensitivity Acquisition in Large Language Models

arXiv:2601.03079v4 Announce Type: replace Abstract: Moral sensitivity is the most fundamental capability underlying human moral competence. Although many approaches aim to align large language models (LLMs) with human moral values, they primarily focus on fitting the distributions of morally appropriate texts while overlooking how to enable moral sensitivity acquisition in LLMs. In this paper, we take a step toward addressing the question: How can moral sensitivity be acquired in LLMs? Specifically, we propose a pragmatic inference approach that facilitates moral sensitivity acquisition in LLM

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