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

• URL: http://arxiv.org/abs/2411.13244v1
• Date: Wed, 20 Nov 2024 12:03:17 GMT
• Title: Leveraging Prior Experience: An Expandable Auxiliary Knowledge Base for Text-to-SQL
• Authors: Zhibo Chu, Zichong Wang, Qitao Qin,
• Abstract summary: 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-context. We propose LPE-Leveraging, a novel framework designed to augment LLMs by enabling continual learning without requiring fine-tuning. Our experimental results demonstrate that this continual learning approach yields substantial performance gains.
• Score: 0.5735035463793009
• License:
• 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 (Leveraging Prior Experience: An Expandable Auxiliary Knowledge Base for Text-to-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.

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