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

• URL: http://arxiv.org/abs/2601.03079v1
• Date: Tue, 06 Jan 2026 15:09:05 GMT
• Title: Learning to Diagnose and Correct Moral Errors: Towards Enhancing Moral Sensitivity in Large Language Models
• Authors: Bocheng Chen, Han Zi, Xi Chen, Xitong Zhang, Kristen Johnson, Guangliang Liu,
• Abstract summary: We propose two pragmatic inference methods that faciliate LLMs to diagnose morally benign and hazardous input and correct moral errors.<n>A central strength of our pragmatic inference methods is their unified perspective for designing pragmatic inference procedures grounded in their inferential loads.
• Score: 8.691489065712316
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Moral sensitivity is fundamental to human moral competence, as it guides individuals in regulating everyday behavior. Although many approaches seek to align large language models (LLMs) with human moral values, how to enable them morally sensitive has been extremely challenging. In this paper, we take a step toward answering the question: how can we enhance moral sensitivity in LLMs? Specifically, we propose two pragmatic inference methods that faciliate LLMs to diagnose morally benign and hazardous input and correct moral errors, whereby enhancing LLMs' moral sensitivity. A central strength of our pragmatic inference methods is their unified perspective: instead of modeling moral discourses across semantically diverse and complex surface forms, they offer a principled perspective for designing pragmatic inference procedures grounded in their inferential loads. Empirical evidence demonstrates that our pragmatic methods can enhance moral sensitivity in LLMs and achieves strong performance on representative morality-relevant benchmarks.

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