We propose a novel approach for generating complex outputs that significantly improves accuracy in text-to-SQL tasks. Our method leverages execution results to select the most semantically consistent query from multiple candidates, enabling smaller, cost-effective models to surpass computationally intensive reasoning methods such as o1, o3-mini, and DeepSeek R1 while reducing inference cost by as much as 30 times. It integrates effortlessly with existing models, offering a practical and scalable pathway to state-of-the-art SQL generation.

翻译：我们提出了一种生成复杂输出的新方法，该方法显著提升了文本到SQL任务的准确性。我们的方法利用执行结果从多个候选查询中选择语义最一致的查询，使得规模较小、成本效益更高的模型能够超越计算密集型的推理方法（如o1、o3-mini和DeepSeek R1），同时将推理成本降低多达30倍。该方法能够与现有模型无缝集成，为实现最先进的SQL生成提供了一条实用且可扩展的路径。