Text-to-SQL is a subtask in semantic parsing that has seen rapid progress with the evolution of Large Language Models (LLMs). However, LLMs face challenges due to hallucination issues and a lack of domain-specific database knowledge(such as table schema and cell values). As a result, they can make errors in generating table names, columns, and matching values to the correct columns in SQL statements. This paper introduces a method of knowledge injection to enhance LLMs' ability to understand schema contents by incorporating prior knowledge. This approach improves their performance in Text-to-SQL tasks. Experimental results show that pre-training LLMs on domain-specific database knowledge and fine-tuning them on downstream Text-to-SQL tasks significantly improves the Execution Match (EX) and Exact Match (EM) metrics across various models. This effectively reduces errors in generating column names and matching values to the columns. Furthermore, the knowledge-injected models can be applied to many downstream Text-to-SQL tasks, demonstrating the generalizability of the approach presented in this paper.

本研究解决了大型语言模型在文本到SQL任务中的幻觉问题和领域特定数据库知识缺乏的挑战。提出了一种知识注入的方法，通过融入先前知识，提升了模型对表结构内容的理解能力，从而显著提高了执行匹配和精确匹配的效果，减小了生成列名和匹配值的错误。此方法可广泛应用于多种下游文本到SQL任务，展示了其良好的推广能力。

通过领域数据库知识注入增强大型语言模型的文本到SQL能力