Double Triangle Annotation: A Scalable Human-in-the-Loop Framework for High-Precision Historical Document Annotation
arXiv:2605.25781v1 Announce Type: new Abstract: Evaluating structured-information extraction from historical documents at scale requires high-precision ground-truth annotations, yet traditional manual labeling is expensive and fully automated pipelines built on large language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 anno
This development addresses the critical and immediate need for scalable, high-precision data annotation methods, which is a significant bottleneck for AI development across various domains, especially with the increasing volume of unstructured data.
High-precision data annotation is fundamental for the performance and reliability of AI systems, and this framework offers a scalable solution to a long-standing challenge, impacting the efficiency and cost of AI model development.
The adoption of such human-in-the-loop frameworks will significantly reduce the cost and time associated with creating high-quality datasets for AI, enabling faster iteration and deployment of more accurate models, particularly in complex domains like historical document analysis.
- · AI development companies
- · Libraries and archives
- · Data annotation services
- · Research institutions
- · Traditional manual annotation services
- · Companies reliant on low-quality datasets
Improved accuracy and reliability of AI systems trained on higher quality, more extensive datasets.
Acceleration of research and application development in fields heavily dependent on structured information extraction from unstructured sources, such as history, legal, and biomedical.
Potential for new AI applications in previously infeasible data-rich, but annotation-poor, domains due to the reduced cost and increased scalability of ground-truth creation.
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