Efficient schema-less text-to-SQL conversion using large language models

Abstract

Large language models (LLMs) are increasingly being applied to several tasks including text-to-SQL (the process of converting natural language to SQL queries). While most studies revolve around training LLMs on large SQL corpora for better generalization and then perform prompt engineering during inference, we investigate the notion of training LLMs for schema-less prompting. In particular, our approach uses simple natural language questions as input without any additional knowledge about the database schema. By doing so, we demonstrate that smaller models paired with simpler prompts result in considerable performance improvement while generating SQL queries. Our model, based on the Flan-T5 architecture, achieves logical form accuracy (LFA) of 0.85 on the MIMICSQL dataset, significantly outperforming current state-of-the-art models such as Defog-SQL-Coder, GPT-3.5-Turbo, LLaMA-2-7B and GPT-4. This approach reduces the model size, lessening the amount of data and infrastructure cost required for training and serving, and improves the performance to enable the generation of much complex SQL queries.