Addressing Data Scarcity: Augmentation Strategies in Text-to-SQL - Aníbal Nunes Brito, Felipe; Braghetto, Kelly Rosa (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2026: Anais do XLI Simpósio Brasileiro de Bancos de Dados, 2026)
This work explores data augmentation as a strategy to overcome the scarcity of training data for Text-to-SQL translation models in low-resource domains. Six synthetic data generation strategies were applied to a novel dataset containing questions and SQL queries on Brazilian educational and geographic data. Experiments fine-tuning the LLM Mistral 7B demonstrated that using synthetic training data yields superior performance compared to using only original data. Furthermore, combining two augmented datasets allowed for even greater performance gains across several standard literature metrics, in addition to enabling the model to translate queries of greater structural complexity that were not handled by the models without augmented data.