ArcGPT: A Large Language Model Tailored for Real-world Archival Applications
This work addresses the problem of managing and analyzing massive archival data for archivists, but it is incremental as it applies an existing method (LLM pre-training) to a new domain-specific dataset.
The authors tackled the lack of large language models tailored for archival applications by introducing ArcGPT, the first general-purpose LLM for this field, which outperforms existing state-of-the-art models on a new benchmark of four real-world archival tasks.
Archives play a crucial role in preserving information and knowledge, and the exponential growth of such data necessitates efficient and automated tools for managing and utilizing archive information resources. Archival applications involve managing massive data that are challenging to process and analyze. Although LLMs have made remarkable progress in diverse domains, there are no publicly available archives tailored LLM. Addressing this gap, we introduce ArcGPT, to our knowledge, the first general-purpose LLM tailored to the archival field. To enhance model performance on real-world archival tasks, ArcGPT has been pre-trained on massive and extensive archival domain data. Alongside ArcGPT, we release AMBLE, a benchmark comprising four real-world archival tasks. Evaluation on AMBLE shows that ArcGPT outperforms existing state-of-the-art models, marking a substantial step forward in effective archival data management. Ultimately, ArcGPT aims to better serve the archival community, aiding archivists in their crucial role of preserving and harnessing our collective information and knowledge.