Application of Large Language Models for Digital Libraries
Xiaotong Hu · 2024
Digital library collections are valuable documents, which contain vast amounts of knowledge. The rise of artificial intelligence (AI) technologies, especially Large Language Models (LLM), has made an impact on various domains, and may also shift the focus of digital library services from storing and preserving information to utilizing and extracting knowledge from that information. We expect to add value created by LLMs to existing digital library methods so that users can better leverage the information stored in digital library collections. This research aims to improve access to digital library collections by providing users with classification labels and summaries to help easily find their works of interest leveraging large language models. Two expected major benefits are: (1) Enhancing metadata in the form of classification labels, which will help users discover and use digital library collections more easily. (2) Providing summaries of works stored in the digital library to aid researchers in finding works of interest without having to read the entire content, which will help remediate the information overload problem.