Wenxin Mao
2024
Enhancing Text-to-SQL Parsing through Question Rewriting and Execution-Guided Refinement
Wenxin Mao
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Ruiqi Wang
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Jiyu Guo
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Jichuan Zeng
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Cuiyun Gao
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Peiyi Han
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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.
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Co-authors
- Ruiqi Wang 1
- Jiyu Guo 1
- Jichuan Zeng 1
- Cuiyun Gao 1
- Peiyi Han 1
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