  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.
