QDA-SQL: Data Augmentation Method Boosts Multi-Turn Text-to-SQL Performance

Jul 17, 2026

Researchers have proposed QDA-SQL, a data augmentation technique aimed at improving large language models' performance on multi-turn Text-to-SQL tasks. QDA-SQL generates diverse multi-turn Q&A pairs using LLMs and incorporates validation and correction mechanisms to address ambiguous or unanswerable questions. Experiments show that models fine-tuned with QDA-SQL achieve higher SQL statement accuracy and better handle complex queries. The generation script and test set are publicly available.

Why it matters: This work could improve the reliability of AI-driven data interfaces by addressing challenges in multi-turn database querying with LLMs.

Full story at: arXiv Information Retrieval ↗