ACT-SQL: In-Context Learning for Text-to-SQL with
  Automatically-Generated Chain-of-Thought
Hanchong Zhang Ruisheng Cao Lu Chen Hongshen Xu Kai Yu
Recently Large Language Models (LLMs) have been proven to have strong
abilities in various domains and tasks. We study the problem of prompt
designing in the text-to-SQL task and attempt to improve the LLMs' reasoning
ability when generating SQL queries. Besides the trivial few-shot in-context
learning setting, we design our chain-of-thought (CoT) prompt with a similar
method to schema linking. We provide a method named ACT-SQL to automatically
generate auto-CoT exemplars and thus the whole process doesn't need manual
labeling. Our approach is cost-saving since we only use the LLMs' API call once
when generating one SQL query. Furthermore, we extend our in-context learning
method to the multi-turn text-to-SQL task. The experiment results show that the
LLMs' performance can benefit from our ACT-SQL approach. Our approach achieves
SOTA performance on the Spider dev set among existing in-context learning
approaches.