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 · 2023
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 1 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.