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arXiv · 2503.17620

A Case Study of Scalable Content Annotation Using Multi-LLM Consensus and Human Review

Mingyue Yuan·Jieshan Chen·Zhenchang Xing·Gelareh Mohammadi·Aaron Quigley
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Abstract

Content annotation at scale remains challenging, requiring substantial human expertise and effort. This paper presents a case study in code documentation analysis, where we explore the balance between automation efficiency and annotation accuracy. We present MCHR (Multi-LLM Consensus with Human Review), a novel semi-automated framework that enhances annotation scalability through the systematic integration of multiple LLMs and targeted human review. Our framework introduces a structured consensus-building mechanism among LLMs and an adaptive review protocol that strategically engages human expertise. Through our case study, we demonstrate that MCHR reduces annotation time by 32% to 100% compared to manual annotation while maintaining high accuracy (85.5% to 98%) across different difficulty levels, from basic binary classification to challenging open-set scenarios.

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Mingyue Yuan, Jieshan Chen, Zhenchang Xing, Gelareh Mohammadi, Aaron Quigley. 2025-04-28. A Case Study of Scalable Content Annotation Using Multi-LLM Consensus and Human Review. https://arxiv.org/abs/2503.17620

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