用AI智能生成数据库预测任务的有用特征，提升准确率。
ReFuGe: Feature Generation for Prediction Tasks on Relational Databases with LLM Agents
- 分三步走：选表选列、生成特征、筛选保留，全程用大模型代理自动完成
- 在多个真实数据库上测试，预测性能显著优于传统方法
- 适合需要从复杂表结构中挖掘特征的开发者和数据科学家
关系型数据库（RDBs）在众多实际网络应用中支撑跨多表的数据管理。除了常规查询任务，针对RDB的预测任务近年受到关注。本文提出ReFuGe，一种基于大语言模型代理的智能特征生成框架，通过生成有信息量的关联特征来提升预测效果。该任务挑战在于需对复杂模式进行推理，并探索组合爆炸式的特征空间，且无显式监督信号。ReFuGe采用三个专用代理：（1）模式选择代理识别与任务相关的表和列；（2）特征生成代理从选定模式中生成多样化候选特征；（3）特征过滤代理通过基于推理和验证的双重方式评估并保留优质特征。整个过程在迭代反馈循环中持续运行直至性能收敛。在多个RDB基准数据集上的实验表明，ReFuGe显著提升了各类预测任务的表现。代码与数据集已开源于https://github.com/K-Kyungho/REFUGE。
原文摘要 · Abstract (English)
Relational databases (RDBs) play a crucial role in many real-world web applications, supporting data management across multiple interconnected tables. Beyond typical retrieval-oriented tasks, prediction tasks on RDBs have recently gained attention. In this work, we address this problem by generating informative relational features that enhance predictive performance. However, generating such features is challenging: it requires reasoning over complex schemas and exploring a combinatorially large feature space, all without explicit supervision. To address these challenges, we propose ReFuGe, an agentic framework that leverages specialized large language model agents: (1) a schema selection agent identifies the tables and columns relevant to the task, (2) a feature generation agent produces diverse candidate features from the selected schema, and (3) a feature filtering agent evaluates and retains promising features through reasoning-based and validation-based filtering. It operates within an iterative feedback loop until performance converges. Experiments on RDB benchmarks demonstrate that ReFuGe substantially improves performance on various RDB prediction tasks. Our code and datasets are available at https://github.com/K-Kyungho/REFUGE.
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