SQL-Guided Semantic Variation for Data Augmentation in Text-to-SQL - O. Nascimento, Isaque; Braghetto, Kelly (Anais Estendidos do Simpósio Brasileiro de Banco de Dados (SBBD); 2026: Anais Estendidos do XLI Simpósio Brasileiro de Bancos de DadosDO - 10.5753/sbbd_estendido.2026.249637, 2026)
Text-to-SQL maps natural language questions to SQL queries. LLMs have become a promising solution for this task, but question-SQL pairs are hard to produce. Data augmentation can expand training datasets, but many existing approaches focus on paraphrasing questions while preserving the original intent. This work proposes a SQL-guided augmentation technique that creates new examples through controlled semantic changes. The method modifies SQL components, such as columns, operators and values, while preserving query validity, and then aligns the natural language question with the transformed SQL using LLM support. The goal is to increase semantic diversity in Text-to-SQL datasets, while future work will evaluate its impact on model performance.