Analysis of Large Language Model Applications in Public Data Development and Utilization

As data-driven decision-making becomes increasingly important, the development and utilization of public data play a critical role in social governance and economic growth. However, public data often suffer from issues such as large scale, structural complexity, and inconsistent quality, which traditional data processing methods struggle to address effectively. In recent years, breakthroughs in large language models (LLMs) within the field of natural language processing have introduced new opportunities for processing and analyzing public data. This paper explores key aspects of public data development and utilization, focusing on typical application scenarios of LLMs in data cleaning, insight extraction, privacy protection, and compliance review. Additionally, it analyzes the technical limitations and ethical challenges associated with these models. Based on this analysis, the paper proposes suggestions for optimizing model capabilities, reducing resource costs, and establishing standardized application frameworks. Lastly, it anticipates the development prospects of LLMs in multimodal data processing and cross-domain collaborative analysis, aiming to provide theoretical support and practical guidance for applying LLMs in public data development.