Query and Conquer: Execution-Guided SQL Generation
This provides a scalable solution for text-to-SQL tasks, enabling cost-effective models to achieve state-of-the-art performance.
The paper tackles the problem of generating accurate SQL queries from text by using execution results to select the most consistent query, achieving a 30x reduction in inference cost while surpassing methods like o1 and DeepSeek R1.
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.