mlsanityData to 13 Oct 2026

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

arXiv 2607.20455 ↗First posted 14 May 2026cs.CL cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 11 of its 20 references that are in the map. First posted on arXiv 14 May 2026.

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References · 11

  1. 2022Training language models to follow instructions with human feedbackcited by 15,856
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,492
  3. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,412
  4. 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,031
  5. 2023ChatGPT Outperforms Crowd-Workers for Text-Annotation Taskscited by 857
  6. 2024A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applicationscited by 430
  7. 2024Large Language Models for Data Annotation and Synthesis: A Surveycited by 204
  8. 2023AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotatorscited by 172
  9. 2023CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotationcited by 93
  10. 2023Wider and Deeper LLM Networks are Fairer LLM Evaluatorscited by 92
  11. 2024Mirror: A Multiple-perspective Self-Reflection Method for Knowledge-rich Reasoningcited by 10

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