HI-SQL: Optimizing Text-to-SQL Systems through Dynamic Hint Integration

TL;DR

HI-SQL introduces a dynamic hint generation mechanism that leverages historical query logs to improve the accuracy and efficiency of Text-to-SQL systems, reducing reliance on multi-step pipelines and human prompts.

Contribution

The paper presents a novel hint integration approach that uses historical query logs to guide SQL generation, enhancing performance and efficiency in Text-to-SQL tasks.

Findings

Significant improvement in query accuracy on benchmark datasets

Reduction in computational costs and latency

Effective handling of complex multi-table and nested queries

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.