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

arXiv: https://arxiv.org/abs/2503.17620 · HTML: https://mlsanity.com/paper/2503.17620/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2025.

## Built on by (most cited first, 7 of 7)

- [RIGOURATE: Quantifying Scientific Exaggeration with Evidence-Aligned Claim Evaluation](https://mlsanity.com/paper/2601.04350/) (2026)
- [AI-assisted Protocol Information Extraction For Improved Accuracy and Efficiency in Clinical Trial Workflows](https://mlsanity.com/paper/2602.00052/) (2026)
- [HyPAC: Cost-Efficient LLMs-Human Hybrid Annotation with PAC Error Guarantees](https://mlsanity.com/paper/2602.02550/) (2026)
- [LLMs Judge Themselves: A Game-Theoretic Framework for Human-Aligned Evaluation](https://mlsanity.com/paper/2510.15746/) (2025)
- [GMP: A Benchmark for Content Moderation under Co-occurring Violations and Dynamic Rules](https://mlsanity.com/paper/2603.01724/) (2026)
- [Double Triangle Annotation: A Scalable Human-in-the-Loop Framework for High-Precision Historical Document Annotation](https://mlsanity.com/paper/2605.25781/) (2026)
- [Beyond Agreement: Scoring Panel-Surfaced Biomedical Entity Candidates for Curator Triage](https://mlsanity.com/paper/2605.30826/) (2026)

## Abstract (arXiv)

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.