用合成数据训练首个关系型基础模型，实现快速数据库适配。
Relational In-Context Learning via Synthetic Pre-training with Structural Prior
- 基于结构因果模型生成无限多样数据库，解决真实数据稀缺问题。
- 在200万+任务上预训练，19个真实任务上达先进少样本性能。
- 轻量架构支持快速推理，适合需要即时数据库适配的场景。
关系型数据库是现代商业的核心，但缺乏类似文本或视觉领域的基础模型。主要障碍在于高质量数据库私密性强、数量稀少且结构异质，难以进行互联网规模预训练。为克服数据稀缺，我们提出RDB-PFN，首个完全通过合成数据训练的关系型基础模型。受先验数据拟合网络（PFNs）启发，其合成数据来自结构因果模型（SCMs），可实现单表推理。我们设计关系先验生成器，从零构建多样化数据库流。在超过200万条单表及关系型任务上预训练后，RDB-PFN可通过真正的上下文学习，瞬间适应新数据库。实验表明，该模型在19个真实世界关系预测任务上表现优异，超越同类表格式基础模型（使用相同DFS线性输入），且采用轻量级架构，推理速度快。代码已开源。
原文摘要 · Abstract (English)
Relational Databases (RDBs) are the backbone of modern business, yet they lack foundation models comparable to those in text or vision. A key obstacle is that high-quality RDBs are private, scarce, and structurally heterogeneous, making internet-scale pre-training infeasible. To overcome this data scarcity, we introduce RDB-PFN, the first relational foundation model trained purely via synthetic data. Inspired by Prior-Data Fitted Networks (PFNs), where synthetic data generated from Structural Causal Models (SCMs) enables reasoning on single tables, we design a Relational Prior Generator to create an infinite stream of diverse RDBs from scratch. Pre-training on over 2 million synthetic single-table and relational tasks, RDB-PFN learns to adapt to any new database instantly via genuine in-context learning. Experiments show that RDB-PFN achieves strong few-shot performance on 19 real-world relational prediction tasks, outperforming state-of-the-art tabular foundation models evaluated on the same DFS-linearized inputs, while using a lightweight architecture and fast inference. The code is available at https://github.com/MuLabPKU/RDBPFN.
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