Divide and Prompt: Chain of Thought Prompting for Text-to-SQL
This addresses the challenge of complex reasoning in text-to-SQL tasks for database query systems, presenting an incremental improvement over existing methods.
The paper tackles the problem of improving text-to-SQL conversion by proposing a new prompting paradigm called Divide-and-Prompt, which divides the task into subtasks and uses chain-of-thought prompting, resulting in higher execution accuracy for large language models.
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.