Automated Archival Descriptions with Federated Intelligence of LLMs
This work addresses the tedious and error-prone task of creating standardized archival descriptions, offering a domain-specific solution for archivists and institutions.
The paper tackled the problem of automating archival metadata generation by introducing a federated optimization approach that unites multiple LLMs, resulting in superior performance in metadata quality and reliability compared to single-model solutions.
Enforcing archival standards requires specialized expertise, and manually creating metadata descriptions for archival materials is a tedious and error-prone task. This work aims at exploring the potential of agentic AI and large language models (LLMs) in addressing the challenges of implementing a standardized archival description process. To this end, we introduce an agentic AI-driven system for automated generation of high-quality metadata descriptions of archival materials. We develop a federated optimization approach that unites the intelligence of multiple LLMs to construct optimal archival metadata. We also suggest methods to overcome the challenges associated with using LLMs for consistent metadata generation. To evaluate the feasibility and effectiveness of our techniques, we conducted extensive experiments using a real-world dataset of archival materials, which covers a variety of document types and data formats. The evaluation results demonstrate the feasibility of our techniques and highlight the superior performance of the federated optimization approach compared to single-model solutions in metadata quality and reliability.