EXECUTION-AWARE SELF-REFINING TEXT-TO-SQL GENERATION USING LARGE LANGUAGE MODELS

Abstract

The increasing need for data-driven systems has resulted in the growth of user-friendly database interaction methods, especially for non-technical users. Natural Language to SQL, also known as Text-to-SQL is hence required for converting natural language queries from users into executable SQL statements, allowing users to fetch data from databases more efficiently. Although, it is a challenging task to generate accurate SQL queries due to the complexity of natural language understanding, schema linking and variety of database structures. Recent progress in Large Language Models (LLMs) have sig nificantly improved the traditional Text-to-SQL methods by enhancing their semantic understanding for generalized SQL generation. This thesis proposes an Execution-Aware Self-Refining Text-to-SQL Generation framework using Large Language Models, where generated SQL queries are refined iteratively through execution feedback and context awareness. This framework integrates prompt engineering, RAG based architectures and execution validation to identify syntax errors, logical disparities and failed query results. The study also presents an overview of benchmark Text-to-SQL datasets such as Spider, WikiSQL, BIRD, and CoSQL representing that execution-aware self-refinement enhances SQL generation performance compared to conventional approaches. Moreover, it high lights challenges related to scalability, explainability and computational efficiency towards making a domain-centric Text-to-SQL systems for a broad range of technical and non technical users.