Leading models for the text-to-SQL task heavily rely on proprietary Large Language Models (LLMs), posing concerns over data privacy. Closing the performance gap between small open-source models and large proprietary models is crucial to mitigate this reliance. To this end, we introduce a novel two-stage fine-tuning approach that decomposes the task into two simpler tasks. Through comprehensive evaluation on two large cross-domain datasets and two small LLMs, we show that this approach improves execution accuracy by 3 to 7 percent, effectively aligning the performance of open-source models with their proprietary counterparts.

翻译：文本到SQL任务的领先模型严重依赖专有大语言模型，这引发了对数据隐私的担忧。缩小小型开源模型与大型专有模型之间的性能差距，对于缓解这种依赖至关重要。为此，我们提出了一种新颖的两阶段微调方法，将该任务分解为两个更简单的子任务。通过在两个大型跨领域数据集和两个小型大语言模型上的全面评估，我们证明该方法将执行准确率提高了3%至7%，有效实现了开源模型与专有模型在性能上的对齐。