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Improving Labeling Consistency with Detailed Constitutional Definitions and AI-Driven Evaluation

arXiv cs.CL · 2026-05-26 Cached

This paper proposes an AI-driven workflow that writes detailed constitutional definitions for content moderation categories and uses a frontier LLM to interpret them for more consistent labeling. Evaluated on harassment, hate speech, and non-violent crime, the approach reduces cross-model inconsistency by up to 57x compared to paragraph definitions.

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