  Fine-tuning large language models (LLMs) for specific domain tasks has
achieved great success in Text-to-SQL tasks. However, these fine-tuned models
often face challenges with multi-turn Text-to-SQL tasks caused by ambiguous or
unanswerable questions. It is desired to enhance LLMs to handle multiple types
of questions in multi-turn Text-to-SQL tasks. To address this, we propose a
novel data augmentation method, called QDA-SQL, which generates multiple types
of multi-turn Q\&A pairs by using LLMs. In QDA-SQL, we introduce a novel data
augmentation method incorporating validation and correction mechanisms to
handle complex multi-turn Text-to-SQL tasks. Experimental results demonstrate
that QDA-SQL enables fine-tuned models to exhibit higher performance on SQL
statement accuracy and enhances their ability to handle complex, unanswerable
questions in multi-turn Text-to-SQL tasks. The generation script and test set
are released at https://github.com/mcxiaoxiao/QDA-SQL.
