QDA-SQL: Questions Enhanced Dialogue Augmentation for Multi-Turn Text-to-SQL
<|reference_start|>QDA-SQL: Questions Enhanced Dialogue Augmentation for Multi-Turn Text-to-SQL: 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.<|reference_end|>
@article{sun2024qda-sql:,
    title={QDA-SQL: Questions Enhanced Dialogue Augmentation for Multi-Turn
  Text-to-SQL},
    author={Yinggang Sun, Ziming Guo, Haining Yu, Chuanyi Liu, Xiang Li, Bingxuan
  Wang, Xiangzhan Yu, Tiancheng Zhao},
    journal={arXiv preprint arXiv:2406.10593},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.10593},
    primaryClass={cs.AI cs.DB cs.IR}
}