mlsanityData to 7 Oct 2026

A Unified Moral-Value Dataset for Instruction Tuning

arXiv 2607.21279 ↗First posted 23 Jul 2026cs.CLcited by 0

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Abstract (arXiv)

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 https://huggingface.co/datasets/teohzzh/value-for-instruction-tuning.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 34 references that are in the map. First posted on arXiv 23 Jul 2026.

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References · 23

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11 more
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