While fine-tuned large language models (LLMs) excel in generating
grammatically valid SQL in Text-to-SQL parsing, they often struggle to ensure
semantic accuracy in queries, leading to user confusion and diminished system
usability. To tackle this challenge, we introduce SQLFixAgent, an innovative
multi-agent collaborative framework designed for detecting and repairing
erroneous SQL. Our framework comprises a core agent, SQLRefiner, alongside two
auxiliary agents: SQLReviewer and QueryCrafter. The SQLReviewer agent employs
the rubber duck debugging method to identify potential semantic mismatches
between SQL statement and user query. If the error is detected, the
QueryCrafter agent generates multiple SQL statements as candidate repairs using
a fine-tuned SQLTool. Subsequently, leveraging similar repair retrieval and
failure memory reflexion, the SQLRefiner agent selects the most fitting SQL
statement from the candidates as the final repair. We evaluated our proposed
framework on five Text-to-SQL benchmarks. The experimental results show that
our method consistently enhances the performance of the baseline model,
specifically achieving an execution accuracy improvement of over 3\% on the
Bird benchmark. Our framework also has a higher token efficiency compared to
other advanced methods, making it more competitive.

通过引入 SQLFixAgent 创新的多智能体协作框架，我们解决了大型预训练语言模型在生成 SQL 时语法合理但语义不准确的问题，从而提高了用户理解和系统可用性。