  Text-to-SQL aims at generating SQL queries for the given natural language
questions and thus helping users to query databases. Prompt learning with large
language models (LLMs) has emerged as a recent approach, which designs prompts
to lead LLMs to understand the input question and generate the corresponding
SQL. However, it faces challenges with strict SQL syntax requirements. Existing
work prompts the LLMs with a list of demonstration examples (i.e. question-SQL
pairs) to generate SQL, but the fixed prompts can hardly handle the scenario
where the semantic gap between the retrieved demonstration and the input
question is large. In this paper, we propose a retrieval-augmented prompting
method for a LLM-based Text-to-SQL framework, involving sample-aware prompting
and a dynamic revision chain. Our approach incorporates sample-aware
demonstrations, which include the composition of SQL operators and fine-grained
information related to the given question. To retrieve questions sharing
similar intents with input questions, we propose two strategies for assisting
retrieval. Firstly, we leverage LLMs to simplify the original questions,
unifying the syntax and thereby clarifying the users' intentions. To generate
executable and accurate SQLs without human intervention, we design a dynamic
revision chain which iteratively adapts fine-grained feedback from the
previously generated SQL. Experimental results on three Text-to-SQL benchmarks
demonstrate the superiority of our method over strong baseline models.
