Knowledge Distillation for Low-Resource Open-source Text-to-SQL Model

This paper proposes a knowledge-aware Text-to-SQL framework to convert natural language questions into executable SQL queries, even in low-resource settings. It addresses challenges like scarce annotated data and opaque schema definitions by injecting task-specific knowledge into both training and inference.