无需微调，高效解析海量数据库的自然语言查询。
RASL: Retrieval Augmented Schema Linking for Massive Database Text-to-SQL
- 将数据库模式拆解为语义单元，分层检索以提升准确率。
- 在多结构数据库上保持高召回与高精度，有效控制上下文开销。
- 适合需要跨企业部署、无需定制训练的文本转SQL场景。
尽管基于大语言模型的自然语言数据库接口取得进展，但在企业级数据目录上的可扩展性仍是未充分探索的挑战。以往方法依赖领域特定微调，增加部署复杂性，且未能利用数据库元数据中的重要语义信息。为此，我们提出一种组件化检索架构，将数据库模式与元数据分解为离散语义单元并分别索引，实现针对性检索。该方法优先保证表识别准确性，同时利用列级信息，在保持可管理上下文预算的前提下提升性能。实验表明，该系统在结构多样、元数据丰富的大规模数据库上优于基线模型，具备高召回率和高准确率。本方案无需特殊微调即可在多种企业环境中部署，有效填补了自然语言数据库接口中的可扩展性空白。
原文摘要 · Abstract (English)
Despite advances in large language model (LLM)-based natural language interfaces for databases, scaling to enterprise-level data catalogs remains an under-explored challenge. Prior works addressing this challenge rely on domain-specific fine-tuning - complicating deployment - and fail to leverage important semantic context contained within database metadata. To address these limitations, we introduce a component-based retrieval architecture that decomposes database schemas and metadata into discrete semantic units, each separately indexed for targeted retrieval. Our approach prioritizes effective table identification while leveraging column-level information, ensuring the total number of retrieved tables remains within a manageable context budget. Experiments demonstrate that our method maintains high recall and accuracy, with our system outperforming baselines over massive databases with varying structure and available metadata. Our solution enables practical text-to-SQL systems deployable across diverse enterprise settings without specialized fine-tuning, addressing a critical scalability gap in natural language database interfaces.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。