Research of the procedure for converting text into sql based on large language models (LLM) through cross-domaine semantic analysis

Abstract

Theme of work. Research on the Text-to-SQL conversion procedure based on Large Language Models (LLM) through Cross-Domain Semantic Analysis. Purpose of work. To enhance the accuracy and adaptability of Text-to-SQL conversion using Large Language Models (LLM) through cross-domain semantic analysis, enabling reliable query interpretation across various domains and database structures. Methods of research. Comparative analysis, experimental evaluation, cross-domain semantic testing. Results. The research demonstrates that optimized prompt engineering and fine-tuning significantly improve the accuracy and cross-domain adaptability of Large Language Models for Text-to-SQL conversion. Conclusions. This study confirms that Large Language Models (LLMs) can effectively enhance the Text-to-SQL conversion process when optimized with targeted prompt engineering and fine-tuning. Cross-domain semantic analysis proved essential for enabling LLMs to handle varied database structures and domain-specific terminology, improving versatility and accuracy. The findings highlight the potential of LLMs to make SQL query generation more accessible to non-technical users, promoting broader application of AI in database management. Future work may focus on further refining these models to reduce computational costs and increase processing efficiency.