Go to ICLR 2025 Conference homepageStructure-Guided Large Language Models for Text-to-SQL Generation
Recent advancements in large language models (LLMs) have shown promise in bridging the gap between natural language queries and database management systems, enabling users to interact with databases without the background of SQL. However, LLMs often struggle to fully exploit and comprehend the user intention and complex structures of databases. Decomposition-based methods have been proposed to enhance the performance of LLMs on complex tasks, but decomposing SQL generation into subtasks is non-trivial due to the declarative structure of SQL syntax and the intricate connections between query concepts and database elements. In this paper, we propose a novel $\textbf{S}$tructure $\textbf{GU}$ided text-to-$\textbf{SQL}$ framework ($\textbf{SGU-SQL}$) that incorporates syntax-based prompting to enhance the SQL generation capabilities of LLMs. Specifically, SGU-SQL establishes structure-aware links between user queries and database schema and recursively decomposes the complex generation task using syntax-based prompting to guide LLMs in incrementally constructing target SQLs. Extensive experiments on two benchmark datasets demonstrate that SGU-SQL consistently outperforms state-of-the-art text-to-SQL baselines. These results highlight the importance of incorporating structural syntax information for effective text-to-SQL generation and pave the way for more robust and reliable interfaces to databases in the era of artificial intelligence.