Self-correction in text-to-SQL is the process of prompting large language model (LLM) to revise its previously incorrectly generated SQL, and commonly relies on manually crafted self-correction guidelines by human experts that are not only labor-intensive to produce but also limited by the human ability in identifying all potential error patterns in LLM responses. We introduce MAGIC, a novel multi-agent method that automates the creation of the self-correction guideline. MAGIC uses three specialized agents: a manager, a correction, and a feedback agent. These agents collaborate on the failures of an LLM-based method on the training set to iteratively generate and refine a self-correction guideline tailored to LLM mistakes, mirroring human processes but without human involvement. Our extensive experiments show that MAGIC's guideline outperforms expert human's created ones. We empirically find out that the guideline produced by MAGIC enhances the interpretability of the corrections made, providing insights in analyzing the reason behind the failures and successes of LLMs in self-correction. All agent interactions are publicly available at https://huggingface.co/datasets/microsoft/MAGIC.

翻译：文本到SQL中的自校正是指提示大型语言模型（LLM）修正其先前错误生成的SQL的过程，通常依赖于人工专家手动构建的自校正指导原则。这种方法不仅构建过程劳动密集，而且受限于人类识别LLM响应中所有潜在错误模式的能力。我们提出了MAGIC，一种新颖的多智能体方法，用于自动化创建自校正指导原则。MAGIC使用三个专用智能体：一个管理器、一个校正器和一个反馈器。这些智能体基于LLM方法在训练集上的失败案例进行协作，迭代生成并优化针对LLM错误定制的自校正指导原则，模拟了人类流程但无需人工参与。我们的大量实验表明，MAGIC生成的指导原则优于专家人工创建的指导原则。我们通过实证发现，MAGIC生成的指导原则增强了校正的可解释性，为分析LLM在自校正中失败与成功的原因提供了见解。所有智能体交互过程均在 https://huggingface.co/datasets/microsoft/MAGIC 公开提供。