RE-AD: Real-Time Requirement Adherence for Data Labeling
For organizations relying on crowd-sourced annotations, this provides a practical method to improve labeling quality in real-time.
This work introduces a real-time framework using LLMs to validate data labeling quality by decomposing SOPs into atomic rules, achieving an F1 of 0.749 on a synthetic benchmark and 82% error acceptance in production.
Human-annotated data remains fundamental to training frontier Large Language Models (LLMs). However, crowd-sourced annotations often suffer from quality issues stemming from annotator misunderstanding or lack of engagement. To address this, we introduce a real-time requirement adherence (RE-AD) framework that leverages LLMs to proactively validate labeling quality. Our methodology involves decomposing Standard Operating Procedures (SOPs) into atomic rules via self-reflection, categorizing them by complexity, and applying tiered validation strategies. Evaluated on a synthetic benchmark, the system achieved an F1 score of 0.749. Furthermore, production deployment resulted in annotators accepting and fixing 82% of the errors flagged by the framework. We include ablation studies to demonstrate the impact of our core design decisions.