ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-Thought

• URL: http://arxiv.org/abs/2310.17342v1
• Date: Thu, 26 Oct 2023 12:16:25 GMT
• Title: ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-Thought
• Authors: Hanchong Zhang, Ruisheng Cao, Lu Chen, Hongshen Xu, Kai Yu
• Abstract summary: Large Language Models (LLMs) have been proven to have strong abilities in various domains and tasks. We design our chain-of-thought (CoT) prompt with a similar method to schema linking. We extend our in-context learning method to the multi-turn text-to-context task.
• Score: 24.1320473171017
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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