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arXiv · 2607.20455

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

Siddarth Malreddy·Ishan Nigam·Akshay Arora·Nikhil Mittal·Subrat Sahu
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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.

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Siddarth Malreddy, Ishan Nigam, Akshay Arora, Nikhil Mittal, Subrat Sahu. 2026-05-14. RE-AD: Real-Time Requirement Adherence for Data Labeling. https://arxiv.org/abs/2607.20455

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