A Unified Moral-Value Dataset for Instruction Tuning

Abstract:Large language models (LLMs) have developed rapidly and become valuable tools in everyday life. However, how to align LLMs to a particular set of human values is still an open problem. Recent studies show that instruction tuning has strong potential for zero-shot tasks and may serve as an effective approach to addressing value alignment. Nevertheless, although many datasets for instruction tuning already exist, they are not specifically designed around moral scenarios and behaviors. We construct a unified moral-value dataset that can be directly used for instruction tuning. This dataset is built upon existing moral-value datasets by merging them into a unified corpus and converting them into an instruction-response format. We show that training on a mixed dataset combining general task datasets with our dataset preserves general-task performance, and we report preliminary observations on how the mixing ratio affects value-oriented task performance. Our work provides a moral-value dataset for instruction tuning and offers a useful resource for further alignment research. The dataset is available at this https URL.

Computation and Language (cs.CL)

arXiv:2607.21279 [cs.CL]

(or arXiv:2607.21279v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.21279 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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