Leveraging Prior Experience: An Expandable Auxiliary Knowledge Base for Text-to-SQL

Zhibo Chu, Zichong Wang, Qitao Qin

Keywords: Large language models; Text to SQL; In-context learning; Continuous learning;

Abstract: Large Language Models (LLMs) exhibit impressive problem-solving skills across many tasks, but they still underperform compared to humans in various downstream applications, such as text-to-SQL. On the BIRD benchmark leaderboard, human performance achieves an accuracy of 92.96\%, whereas the top-performing method reaches only 72.39\%. Notably, these state-of-the-art (SoTA) methods predominantly rely on in-context learning to simulate human-like reasoning. However, they overlook a critical human skill: continual learning. Inspired by the educational practice of maintaining mistake notebooks during our formative years, we propose LPE-SQL ($\underline{\textbf{L}}$everaging $\underline{\textbf{P}}$rior $\underline{\textbf{E}}$xperience: An Expandable Auxiliary Knowledge Base for Text-to-$\underline{\textbf{SQL}}$), a novel framework designed to augment LLMs by enabling continual learning without requiring parameter fine-tuning. LPE-SQL consists of four modules that $\textbf{i)}$ retrieve relevant entries, $\textbf{ii)}$ efficient sql generation, $\textbf{iii)}$ generate the final result through a cross-consistency mechanism and $\textbf{iv)}$ log successful and failed tasks along with their reasoning processes or reflection-generated tips. Importantly, the core module of LPE-SQL is the fourth one, while the other modules employ foundational methods, allowing LPE-SQL to be easily integrated with SoTA technologies to further enhance performance. Our experimental results demonstrate that this continual learning approach yields substantial performance gains, with the smaller Llama-3.1-70B model with surpassing the performance of the larger Llama-3.1-405B model using SoTA methods.

Primary Area: generative models

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Submission Number: 3652