RE-AD: Real-Time Requirement Adherence for Data Labeling

Abstract: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.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2607.20455 [cs.CL]

(or arXiv:2607.20455v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.20455 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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