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 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.

翻译：实施档案标准需要专业知识，而手动为档案材料创建元数据描述是一项繁琐且易出错的任务。本研究旨在探索智能体人工智能与大语言模型在应对标准化档案描述流程实施挑战方面的潜力。为此，我们引入了一种基于智能体人工智能的系统，用于自动化生成高质量的档案材料元数据描述。我们开发了一种联邦优化方法，该方法融合多个大语言模型的智能以构建最优档案元数据。我们还提出了克服使用大语言模型进行一致性元数据生成相关挑战的方法。为评估所提技术的可行性与有效性，我们使用真实世界的档案材料数据集进行了大量实验，该数据集涵盖多种文档类型与格式。评估结果证明了我们技术的可行性，并突显了联邦优化方法在元数据质量与可靠性方面相较于单模型解决方案的优越性能。