Reinforcement learning to enable reasoning LLMs for Text2SQL

The ability to interact with complex databases using natural language (NL) is a key step in democratizing data access, a long-standing goal in the enterprise world. While Large Language Models (LLMs) have shown remarkable promise in translating NL questions into SQL queries (Text2QL), their performance stall when faced with the complexities of real-world enterprise databases. This talk will report a promising solution to enhance the reasoning capabilities of LLMs for this task. Our "Think2SQL" methodology investigates various strategies for improving LLM performance, including Zero-Shot Learning (ZSL), Supervised Fine-Tuning (SFT), and Reinforcement Learning (RL). RL, using rewards crafted around SQL execution accuracy, significantly boosts the performance of small LLMs, achieving results comparable to those of much larger models on complex datasets. Finally, we will highlight the path forward for Text2SQL systems capable of navigating the nuances of human language, such as ambiguity, in a real-world enterprise context.