arXiv:2509.24102v5 Announce Type: replace 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 infl
The increasing public and academic scrutiny on the ethical implications and biases of large language models necessitates robust solutions for moral reasoning, driving this research forward now.
Improving LLMs' ability to acquire and apply moral reasoning reduces risks of harmful outputs and opens pathways for more trustworthy and widely adopted AI systems across sensitive domains.
LLMs can potentially move beyond statistical correlations to develop a more generalized understanding of moral implications, closing the gap between 'what is said' and 'what is morally implied.'
- · AI developers
- · Ethical AI researchers
- · Industries deploying AI in sensitive applications
- · Users interacting with LLMs
- · Developers of 'black box' AI
- · Companies with unethical AI practices
Increased trustworthiness and broader application of LLMs in fields requiring ethical judgment.
New regulatory frameworks and industry standards may emerge around 'morally aware' AI systems.
The development of truly autonomous AI agents capable of navigating complex ethical dilemmas without explicit human intervention becomes more feasible, accelerating the 'ai-agents' narrative.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL