Jiyu Guo


2024

pdf
Enhancing Text-to-SQL Parsing through Question Rewriting and Execution-Guided Refinement
Wenxin Mao | Ruiqi Wang | Jiyu Guo | Jichuan Zeng | Cuiyun Gao | Peiyi Han | Chuanyi Liu
Findings of the Association for Computational Linguistics ACL 2024

Large Language Model (LLM)-based approach has become the mainstream for Text-to-SQL task and achieves remarkable performance. In this paper, we augment the existing prompt engineering methods by exploiting the database content and execution feedback. Specifically, we introduce DART-SQL, which comprises two key components: (1) Question Rewriting: DART-SQL rewrites natural language questions by leveraging database content information to eliminate ambiguity. (2) Execution-Guided Refinement: DART-SQL incorporates database content information and utilizes the execution results of the generated SQL to iteratively refine the SQL. We apply this framework to the two LLM-based approaches (DAIL-SQL and C3) and test it on four widely used benchmarks (Spider-dev, Spider-test, Realistic and DK). Experiments show that our framework for DAIL-SQL and C3 achieves an average improvement of 12.41% and 5.38%, respectively, in terms of execution accuracy(EX) metric.