综述大模型如何用检索增强生成提升自然语言转SQL的准确率
From Natural Language to SQL: Review of LLM-based Text-to-SQL Systems
- 采用检索增强生成技术，让大模型更精准理解数据库上下文
- 对比多种评测基准与指标，揭示当前最佳实践
- 聚焦图结构检索增强，提升表间关联理解能力
当与检索增强生成（RAG）结合时，大模型在将自然语言查询转换为结构化正确SQL方面显著提升了性能。与以往综述不同，本文全面梳理了基于大模型的文本转SQL系统的发展历程，从早期规则模型到利用RAG的先进方法。文章讨论了常用评测基准、评估方法和指标，并首次研究了图结构RAG在提升上下文准确性与模式链接方面的应用。最后，指出了计算效率、模型鲁棒性及数据隐私等关键挑战，为未来改进提供方向。
原文摘要 · Abstract (English)
LLMs when used with Retrieval Augmented Generation (RAG), are greatly improving the SOTA of translating natural language queries to structured and correct SQL. Unlike previous reviews, this survey provides a comprehensive study of the evolution of LLM-based text-to-SQL systems, from early rule-based models to advanced LLM approaches that use (RAG) systems. We discuss benchmarks, evaluation methods, and evaluation metrics. Also, we uniquely study the use of Graph RAGs for better contextual accuracy and schema linking in these systems. Finally, we highlight key challenges such as computational efficiency, model robustness, and data privacy toward improvements of LLM-based text-to-SQL systems.
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