AI-Driven Workflow Improves Labeling Consistency with Detailed Constitutions
A new AI-driven workflow aims to improve labeling consistency in automated pipelines by generating detailed per-category constitutions. The method addresses the problem of simple category definitions being insufficient for accurate golden labels, as human annotators struggle to hold prescriptive definitions in working memory and often fall back on intuition, causing label drift. The proposed solution uses AI to write a constitution that covers edge cases, and a frontier LLM interprets it for each input to produce consistent labels. The paper demonstrates the efficacy of this approach, which is particularly relevant for content moderation and other classification tasks.
Key facts
- Automated labeling pipelines require accurate, consistent golden labels.
- Simple category definitions are not detailed enough for labelers.
- Prescriptive definitions exceed human working memory, leading to drift.
- AI writes a per-category constitution covering edge cases.
- A frontier LLM interprets the constitution for each input.
- The workflow is demonstrated to be effective.
- Content moderation is a prominent use case.
- The paper is from arXiv:2605.24247.
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