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
The rapid advancement of large language models necessitates a focus on ethical alignment, as their capabilities grow and integration into society increases.
Achieving moral sensitivity in LLMs is crucial for their responsible deployment and to prevent unintended negative societal consequences.
The focus shifts from merely fitting moral text distributions to actively enabling LLMs to diagnose and correct their own ethical shortcomings.
- · AI ethics researchers
- · Companies deploying morally sensitive AI
- · Society at large
- · Developers of unaligned AI systems
- · Applications with high ethical risk
- · Organizations ignoring AI ethics
More robust and trustworthy AI applications will emerge as LLMs become more ethically capable.
Public trust in AI systems will increase, potentially accelerating AI adoption in sensitive domains.
The development of truly 'moral' AI could lead to new philosophical questions about AI personhood and responsibility.
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