Text-to-SQL tasks have gained attractive improvements since the release of ChatGPT. Among them, agent-based frameworks have been widely used in this field. However, the impact of data-centric strategies on text-to-SQL tasks has rarely been explored. In this paper, we systemically design a fully automated data-centric pipeline for text-to-SQL tasks, including \emph{adaptive data repair}, which can automatically find and fix errors in the training dataset; and \emph{error data augmentation}, where we specifically diffuse and enhance erroneous data predicted by the initially trained models. Meanwhile, we propose a Multi-Model collaboration training schema, aiming to train multiple models with different augmented data, enabling them to possess distinct capabilities and work together to complement each other, because it has been found that the capability of a single fine-tuned model is very limited. Furthermore, we utilize an ensemble strategy to integrate the capabilities of multiple models to solve a multiple-choice question, aiming to further improve the accuracy of text-to-SQL tasks. The experiment results and ablation study have demonstrated the effectiveness of data-centric pipeline and Multi-Model(MM) interactive iterative strategies, achieving first place in lightweight text-to-SQL models (within 70B).

翻译：自ChatGPT发布以来，文本到SQL任务取得了显著进展。其中，基于智能体的框架在该领域得到了广泛应用。然而，数据驱动策略对文本到SQL任务的影响却鲜有研究。本文系统性地设计了一个面向文本到SQL任务的全自动数据驱动流程，包括**自适应数据修复**——能够自动发现并修正训练数据集中的错误；以及**错误数据增强**——我们专门对初始训练模型预测的错误数据进行扩散与增强。同时，我们提出了一种多模型协同训练框架，旨在利用不同的增强数据训练多个模型，使它们具备不同的能力并协同工作、相互补充，因为研究发现单一微调模型的能力非常有限。此外，我们采用集成策略来整合多个模型的能力以解决多选题，旨在进一步提升文本到SQL任务的准确率。实验结果与消融研究验证了数据驱动流程与多模型交互式迭代策略的有效性，在轻量级文本到SQL模型（70B参数以内）中取得了最佳性能。