关键词: 模式链接, 大语言模型, Text-to-SQL, 检索增强, 上下文学习

Abstract: To address the limitations of Large Language Models (LLMs) in generating Structured Query Language (SQL) statements for complex multi-table database scenarios, a Multi-Generator SQL framework (MG-SQL) based on collaborative generators was proposed. First, to mitigate noise interference caused by irrelevant schema information in complex databases, the schema linking process was enhanced by generating initial SQL, combined with semantic similarity-based retrieval. Second, to improve the quality and diversity of candidate SQL statements, a multi-strategy collaborative generation framework was developed using refined schema descriptions: 1) the experience generator retrieved dynamic examples; 2) the chain-of-thought generator strengthened logical reasoning; 3) the query planner generator simulated database execution workflows; and 4) the progressive generator performed iterative optimization. Finally, the optimal SQL was ultimately selected through voting mechanisms. A reflective learning mechanism was further proposed, where comparative analysis between generated results and reference SQL formed reflective samples to dynamically construct domain-specific knowledge bases for continuous learning. Evaluations on the BIRD benchmark demonstrated that when employing the lightweight GPT-4o-mini model, the schema linking module achieved 98% Strict Recall Rate (SRR) while effectively filtering 45% redundant columns. The framework attained 69.69% EXecution accuracy (EX) and 79.59% Valid Efficiency Score (VES), outperforming mainstream GPT-4o-based approaches, which validates its practical effectiveness in complex scenarios.

Key words: schema linking, large Language Model (LLM), Text-to- Structured Query Language (SQL), Retrieval-Augmented, In-Context Learning (ICL)