Double Triangle Annotation: A Scalable Human-in-the-Loop Framework for High-Precision Historical Document Annotation
It provides a scalable, high-precision annotation method for historical document processing, reducing manual effort while ensuring accuracy.
The paper introduces Double Triangle Annotation, a human-in-the-loop framework that uses cross-model consensus between two Multimodal Large Language Models to automate annotation, achieving a Word Error Rate of 0.003 on a historical document corpus and auto-accepting over 85% of 13,595 fields.
Evaluating structured-information extraction from historical documents at scale requires high-precision ground-truth annotations, yet traditional manual labeling is expensive and fully automated pipelines built on large language models are prone to hallucination. We propose Double Triangle Annotation, a two-layer human-in-the-loop framework that leverages cross-model consensus to automate the majority of annotation work while ensuring high-precision outputs. In the first layer, two architecturally independent Multimodal Large Language Models annotate each document in parallel; when they agree, the label is auto-accepted, and disagreements are routed to a human jury. A second layer cross-checks two such systems against each other, escalating residual conflicts to a domain expert. The framework rests on a single assumption -- error independence between models -- requires no distributional priors or task-specific calibration, and becomes more autonomous as model capability improves. On the Guides Rosenwald, a corpus of French medical directories spanning 1887-1906, the framework achieves a final Word Error Rate of 0.003. Applied at scale, model consensus auto-accepts over 85% of 13,595 fields. We release the resulting benchmark -- the first structured-extraction ground truth for the Rosenwald Guides -- to support future work on historical document processing.