Large Language Models for Government Document Writing: A Survey

The emergence of Large Language Models (LLMs) has had a significant impact on fields such as question answering (Q&A) and machine understanding. However, due to the specificity of government documents, there are several LLMs exclusively focused on government document writing. In this survey, we thoroughly examine the application of LLMs in the creation and refinement of government documents. A systematic comparison of various models, i.e., ChatGPT-4 and Kimi, is provided here. Our findings reveal that, although LLMs can significantly streamline the document preparation process, there is considerable variation in their effectiveness. ChatGPT-4 stands out as the top-performing model, excelling in context understanding and grammatical accuracy, followed closely by QWen2.5. In contrast, TinyLlama performs limited comprehension of Chinese and exhibits a propensity for English output. This survey has two main contributions: (1) emphasizes the importance of the task of polishing government documents; (2) fills the gap in the market for a comprehensive comparison of LLMs in the task of polishing government documents. Through comparison analysis, this paper aims at giving a reference for future research and practical application of Large Language Models (LLMs) in the government sector. At the same time, it can assure the maximum of potential and lower the risks of it.