Title: HI-SQL: Optimizing Text-to-SQL Systems through Dynamic Hint Integration Show Chinese title

Title: HI-SQL：通过动态提示集成优化文本到SQL系统

Abstract: Text-to-SQL generation bridges the gap between natural language and databases, enabling users to query data without requiring SQL expertise. While large language models (LLMs) have significantly advanced the field, challenges remain in handling complex queries that involve multi-table joins, nested conditions, and intricate operations. Existing methods often rely on multi-step pipelines that incur high computational costs, increase latency, and are prone to error propagation. To address these limitations, we propose HI-SQL, a pipeline that incorporates a novel hint generation mechanism utilizing historical query logs to guide SQL generation. By analyzing prior queries, our method generates contextual hints that focus on handling the complexities of multi-table and nested operations. These hints are seamlessly integrated into the SQL generation process, eliminating the need for costly multi-step approaches and reducing reliance on human-crafted prompts. Experimental evaluations on multiple benchmark datasets demonstrate that our approach significantly improves query accuracy of LLM-generated queries while ensuring efficiency in terms of LLM calls and latency, offering a robust and practical solution for enhancing Text-to-SQL systems. Show Chinese abstract

Abstract: 文本到SQL生成弥合了自然语言和数据库之间的差距，使用户无需SQL专业知识即可查询数据。 虽然大型语言模型（LLMs）在该领域取得了显著进展，但在处理涉及多表连接、嵌套条件和复杂操作的复杂查询时仍存在挑战。 现有方法通常依赖于多步骤流程，这会带来较高的计算成本，增加延迟，并容易出现错误传播。 为了解决这些限制，我们提出了HI-SQL，这是一种结合了新颖提示生成机制的流程，利用历史查询日志来指导SQL生成。 通过分析之前的查询，我们的方法生成上下文提示，专注于处理多表和嵌套操作的复杂性。 这些提示无缝集成到SQL生成过程中，消除了对昂贵的多步骤方法的依赖，并减少了对人工编写提示的依赖。 在多个基准数据集上的实验评估表明，我们的方法显著提高了LLM生成查询的查询准确性，同时在LLM调用和延迟方面确保了效率，为增强文本到SQL系统提供了一个强大且实用的解决方案。