A Unified Moral-Value Dataset for Instruction Tuning
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 bui
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- FuzzyOverlapping authors or contributors · 62%Tongyi-MAI/Z-Image-Turbo →
“Shared author/contributor keys: mai”
- PossiblePossibly related (embedding) · 58%Large language models often prioritize Western moral values, overlooking other cultures - The Conversation →
- PossiblePossibly related (embedding) · 50%Alignment →
- LinkedLinked via arxiv author · 85%Zhaohui Zeng →
“A Unified Moral-Value Dataset for Instruction Tuning”
- LinkedLinked via arxiv author · 85%Florian Mai →
“A Unified Moral-Value Dataset for Instruction Tuning”