Application and Challenge Analysis of Large Language Models in Government Document Management

Abstract

As the global process of information technology advances, the role of document management in government operations becomes increasingly crucial. Governments generate vast amounts of documents, which pose significant challenges to traditional document management systems due to their growing volume and complexity. To address these challenges, the development of Artificial Intelligence (AI) technologies, especially Large Language Models (LLMs), has introduced new possibilities for improving the efficiency of government document management. LLMs models trained on large datasets are capable of understanding and generating text with a high degree of semantic understanding, making them valuable for automated document management, text categorization, information extraction, and natural language querying. This paper discuss the ways of traditional government document management and their limitations, focusing on the application of LLMs to government document management, emphasizing their potential to increase automation of document management, reduce human error, and improve the accuracy and efficiency of data processing. Also discussed the limitations of LLMs in government document management, such as bias in training data, potential errors in generated information, and challenges related to data privacy and legal compliance. LLMs offer powerful auxiliary functions in government document management, further research is needed to address these limitations and ensure responsible and effective use of this technology.