Related papers: Divide and Prompt: Chain of Thought Prompting for Text-to-SQL

Divide and Prompt: Chain of Thought Prompting for Text-to-SQL

URL: http://arxiv.org/abs/2304.11556v1
Date: Sun, 23 Apr 2023 06:52:35 GMT
Title: Divide and Prompt: Chain of Thought Prompting for Text-to-SQL
Authors: Xiping Liu and Zhao Tan
Abstract summary: Chain-of-thought (CoT) prompting combined with large language models (LLMs) have achieved encouraging results on complex reasoning tasks. We propose Divide-and-Prompt, which first divides the task into subtasks, and then approach each subtask through CoT.
Score: 0.03807314298073299
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Chain-of-thought (CoT) prompting combined with large language models (LLMs) have achieved encouraging results on complex reasoning tasks. Text-to-SQL is a critical semantic parsing task that converts natural language questions into SQL statements, involving a complex reasoning process. However, there is little work about using CoT prompting to activate LLM's reasoning capabilities on Text-to-SQL tasks. In this work, we propose a new paradigm for prompting Text-to-SQL tasks, called Divide-and-Prompt, which first divides the task into subtasks, and then approach each subtask through CoT. We present 3 prompting-based methods to enhance the Text-to-SQL ability of LLMs. Experiments show that these prompts guide LLMs to generate Text-to-SQL with higher execution accuracy.

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