SAFE-SQL: Self-Augmented In-Context Learning with Fine-grained Example
  Selection for Text-to-SQL
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.
67b406993d0f54ab381594f5
Jimin Lee
67b406993d0f54ab381594f6
Ingeol Baek
67b406993d0f54ab381594f7
Byeongjeong Kim
67b406993d0f54ab381594f8
Hwanhee Lee
SAFE-SQL: Self-Augmented In-Context Learning with Fine-grained Example
  Selection for Text-to-SQL
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.
SAFE-SQL enhances Text-to-SQL by generating and filtering self-augmented examples, improving execution accuracy in real-world scenarios and hard-to-handle cases.
large language models (LLMs) self-augmentation in-context learning example selection zero-shot few-shot