ArcGPT: A Large Language Model Tailored for Real-world Archival
  Applications
Shitou Zhang Jingrui Hou Siyuan Peng Zuchao Li Qibiao Hu Ping Wang
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.