@inproceedings{malreddy-etal-2026-ad,
title = "{RE}-{AD}: Real-Time Requirement Adherence for Data Labeling",
author = "Malreddy, Siddarth and
Nigam, Ishan and
Arora, Akshay and
Mittal, Nikhil and
Sahu, Subrat",
editor = "Mille, Simon and
Gehrmann, Sebastian and
Schmidtov{\'a}, Patr{\'i}cia and
Du{\v{s}}ek, Ond{\v{r}}ej and
Fadaee, Marzieh and
Lo, Kyle and
Santus, Enrico and
Stanovsky, Gabriel",
booktitle = "Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics ({GEM})",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.gem-main.17/",
pages = "148--154",
ISBN = "979-8-89176-423-1",
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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%0 Conference Proceedings
%T RE-AD: Real-Time Requirement Adherence for Data Labeling
%A Malreddy, Siddarth
%A Nigam, Ishan
%A Arora, Akshay
%A Mittal, Nikhil
%A Sahu, Subrat
%Y Mille, Simon
%Y Gehrmann, Sebastian
%Y Schmidtová, Patrícia
%Y Dušek, Ondřej
%Y Fadaee, Marzieh
%Y Lo, Kyle
%Y Santus, Enrico
%Y Stanovsky, Gabriel
%S Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics (GEM)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-423-1
%F malreddy-etal-2026-ad
%X 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.
%U https://aclanthology.org/2026.gem-main.17/
%P 148-154
Markdown (Informal)
[RE-AD: Real-Time Requirement Adherence for Data Labeling](https://aclanthology.org/2026.gem-main.17/) (Malreddy et al., GEM 2026)
ACL
- Siddarth Malreddy, Ishan Nigam, Akshay Arora, Nikhil Mittal, and Subrat Sahu. 2026. RE-AD: Real-Time Requirement Adherence for Data Labeling. In Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics (GEM), pages 148–154, San Diego, California, USA. Association for Computational Linguistics.