  In-context learning of large-language models (LLMs) has achieved remarkable
success in the field of natural language processing, while extensive case
studies reveal that the single-step chain-of-thought prompting approach faces
challenges such as attention diffusion and inadequate performance in complex
tasks like text-to-SQL. To improve the contextual learning capabilities of LLMs
in text-to-SQL, a workflow paradigm method is proposed, aiming to enhance the
attention and problem-solving scope of LLMs through decomposition.
Specifically, the information determination module for eliminating redundant
information and the brand-new prompt structure based on problem classification
greatly enhance the model's attention. Additionally, the inclusion of
self-correction and active learning modules greatly expands the problem-solving
scope of LLMs, hence improving the upper limit of LLM-based approaches.
Extensive experiments conducted on three datasets demonstrate that our approach
outperforms other methods by a significant margin. About 2-3 percentage point
improvements compared to the existing baseline on the Spider Dev,
Spider-Realistic, and Bird Dev datasets and new SOTA results on the Spider Test
dataset are achieved. Our code is available on GitHub:
\url{https://github.com/FlyingFeather/DEA-SQL}.
