SAFE-SQL: Self-Augmented In-Context Learning with Fine-grained Example
  Selection for Text-to-SQL
Authors: name: Jimin Lee, name: Ingeol Baek, name: Byeongjeong Kim, name: Hwanhee Lee

Text-to-SQL aims to convert natural language questions into executable SQL
queries. While previous approaches, such as skeleton-masked selection, have
demonstrated strong performance by retrieving similar training examples to
guide large language models (LLMs), they struggle in real-world scenarios where
such examples are unavailable. To overcome this limitation, we propose
Self-Augmentation in-context learning with Fine-grained Example selection for
Text-to-SQL (SAFE-SQL), a novel framework that improves SQL generation by
generating and filtering self-augmented examples. SAFE-SQL first prompts an LLM
to generate multiple Text-to-SQL examples relevant to the test input. Then
SAFE-SQL filters these examples through three relevance assessments,
constructing high-quality in-context learning examples. Using self-generated
examples, SAFE-SQL surpasses the previous zero-shot, and few-shot Text-to-SQL
frameworks, achieving higher execution accuracy. Notably, our approach provides
additional performance gains in extra hard and unseen scenarios, where
conventional methods often fail.