Query and Conquer: Execution-Guided SQL Generation
Łukasz Borchmann, Marek Wydmuch
2025-04-01
Summary
This paper is about a new way to create SQL queries (used to get information from databases) that are more accurate, by checking if the query works correctly before choosing the best one.
What's the problem?
It's hard to make accurate SQL queries from text, especially complex ones.
What's the solution?
The researchers developed a method that uses the results of running different query options to pick the one that makes the most sense, leading to more accurate results.
Why it matters?
This work matters because it can improve the accuracy of database queries, making it easier to get the right information from large databases.
Abstract
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.