用简单方法让模型在关联数据库中实现零样本预测。
RDBLearn: Simple In-Context Prediction Over Relational Databases
- 通过关联数据聚合自动构建增强表格，再用现成模型预测。
- 在多个数据库基准上表现优于其他基线，部分超越有监督模型。
- 提供易用接口，支持切换不同模型后端，适合数据库任务开发者。
近年来，表格上下文学习（ICL）表明，单一预训练模型可通过少量标注样例适应新预测任务，避免每任务单独训练和调优。然而，许多真实场景任务存在于关联数据库中，预测信号分散在多个关联表中，而非单一扁平表。我们提出一种简单方法：自动使用目标行关联记录的聚合特征进行建模，生成增强表格，再运行现成的表格基础模型。该方法被封装为 RDBLearn 工具包（https://github.com/HKUSHXLab/rdblearn），具有类似 scikit-learn 的估计器接口，可轻松替换不同表格 ICL 后端；同时提供面向代理的专用接口。在 RelBench 与 4DBInfer 多个数据集上，RDBLearn 是评估中表现最佳的基础模型方法，部分情况下甚至超过在对应数据集上训练或微调的强监督基线。
原文摘要 · Abstract (English)
Recent advances in tabular in-context learning (ICL) show that a single pretrained model can adapt to new prediction tasks from a small set of labeled examples, avoiding per-task training and heavy tuning. However, many real-world tasks live in relational databases, where predictive signal is spread across multiple linked tables rather than a single flat table. We show that tabular ICL can be extended to relational prediction with a simple recipe: automatically featurize each target row using relational aggregations over its linked records, materialize the resulting augmented table, and run an off-the-shelf tabular foundation model on it. We package this approach in \textit{RDBLearn} (https://github.com/HKUSHXLab/rdblearn), an easy-to-use toolkit with a scikit-learn-style estimator interface that makes it straightforward to swap different tabular ICL backends; a complementary agent-specific interface is provided as well. Across a broad collection of RelBench and 4DBInfer datasets, RDBLearn is the best-performing foundation model approach we evaluate, at times even outperforming strong supervised baselines trained or fine-tuned on each dataset.
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