GradeSQL: Test-Time Inference with Outcome Reward Models for Text-to-SQL Generation from Large Language Models

The recent advancements in Text-to-SQL generation using Large Language Models (LLMs) are noteworthy, particularly with the introduction of GradeSQL, which enhances the ability to translate natural language questions into SQL queries. This development is significant as it not only improves the accuracy of SQL generation but also makes database access easier for a broader audience. However, challenges remain with complex queries, prompting the use of innovative test-time strategies like Best-of-N and Majority Voting to refine results. This progress is crucial for democratizing data access and empowering users to interact with databases more effectively.