Enhancing Text-to-SQL With Synthetic Summaries

This approach uses synthetic summaries to enhance few-shot learning in text-to-SQL tasks. It has shown promising results and is simpler than other methods, requiring only the addition of more example summaries to improve performance. The technique was tested on a dataset from BirdSQL, which contains over 1,500 queries working with 95 separate tables, providing complexity that mirrors real-world scenarios. The approach was compared to snippet-based methods, which struggled with irrelevant data, while synthetic summaries maintained their accuracy even when introduced to noise. The quality of the prompt used to generate these summaries significantly impacted performance, with a detailed prompt achieving the best results, finding the right information 91% of the time in the top five results. This technique has potential applications beyond text-to-SQL challenges and highlights the importance of prompt engineering for AI systems.

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