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A Unified Moral-Value Dataset for Instruction Tuning

Research LLMs & Foundation Models

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TL;DR - This paper introduces a unified moral-value dataset formatted for instruction tuning, aiming to support research on aligning LLMs with human values. Preliminary experiments suggest it can be mixed with general-task data without degrading general performance.

  • Consolidates existing moral-value datasets into a single instruction-response corpus.
  • Tests joint training with general-purpose instruction datasets.
  • Reports preliminary evidence that value-task performance depends on the moral-data mixing ratio.
  • Publicly releases the dataset on Hugging Face for further alignment research.

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A Unified Moral-Value Dataset for Instruction Tuning

arXiv cs.CL Zhaohui Zeng, Florian Mai 2026-07-23 arXiv:2607.21279
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-08-21 14:36:42.851810 UTC

TL;DR - This paper introduces a unified moral-value dataset formatted for instruction tuning, aiming to support research on aligning LLMs with human values. Preliminary experiments suggest it can be mixed with general-task data without degrading general performance.

  • Consolidates existing moral-value datasets into a single instruction-response corpus.
  • Tests joint training with general-purpose instruction datasets.
  • Reports preliminary evidence that value-task performance depends on the moral-data mixing ratio.
  • Publicly releases the dataset on Hugging Face for further alignment research.
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