aiqbench analysis
This research proposes pragmatic inference-driven methods to help large language models diagnose and correct moral errors, moving beyond brittle heuristics toward deeper semantic understanding of ethical appropriateness. By varying inference procedures based on inferential load rather than modeling complex distributions separately, the approach demonstrates improved generalization across out-of-the-distribution tasks while maintaining alignment with morally appropriate outputs.
Learning to Diagnose and Correct Errors: Towards Moral Sensitivity Acquisition in Large Language Models
arXiv:2601.03079v5 Announce Type: replace Abstract: Existing approaches to moral value alignment are primarily set out to align LLMs' generation with the distributions of morally appropriate language, which has seen good progress. However, these approaches are often brittle, heavily rely on shallow heuristics, and reduce performance in out-of-the-distribution tasks. In other words, the learning paradigm underlying existing approaches teaches LLMs what morally (in)appropriate language looks like, but not why it is morally (in)appropriate. In this paper, we address this challenge by developing pragmatic inference-driven methods to facilitate LLMs' learning of how to diagnose and correct moral errors, thereby enabling them to generate morally appropriate language. Pragmatic inference is the reasoning process of deriving (implied) meanings -- a famous concept in linguistics. Our methods vary the inference procedures by the inferential load of different moral discourses, rather than modelling their diverse and complex semantic distributions separately. Empirical results demonstrate that our approach improves moral value alignment in LLMs and generalizes effectively across tasks.
Abstract:Existing approaches to moral value alignment are primarily set out to align LLMs' generation with the distributions of morally appropriate language, which has seen good progress. However, these approaches are often brittle, heavily rely on shallow heuristics, and reduce performance in out-of-the-distribution tasks. In other words, the learning paradigm underlying existing approaches teaches LLMs what morally (in)appropriate language looks like, but not why it is morally (in)appropriate. In this paper, we address this challenge by developing pragmatic inference-driven methods to facilitate LLMs' learning of how to diagnose and correct moral errors, thereby enabling them to generate morally appropriate language. Pragmatic inference is the reasoning process of deriving (implied) meanings -- a famous concept in linguistics. Our methods vary the inference procedures by the inferential load of different moral discourses, rather than modelling their diverse and complex semantic distributions separately. Empirical results demonstrate that our approach improves moral value alignment in LLMs and generalizes effectively across tasks.
Computation and Language (cs.CL)
arXiv:2601.03079 [cs.CL]
(or arXiv:2601.03079v5 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2601.03079 Focus to learn more arXiv-issued DOI via DataCite
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