用大模型解决关系型数据库预测任务，效果优于预期。
Tackling prediction tasks in relational databases with LLMs
- 直接应用大模型处理多表关联的复杂数据
- 在RelBench上达到可竞争的预测性能
- 为数据库机器学习提供新基准，适合研究者参考
尽管大语言模型在众多任务中表现卓越，其在关系型数据库预测任务中的应用仍鲜有探索。本文针对大模型难以处理多表关联、复杂关系和异构数据类型的普遍看法，基于最新提出的RelBench基准，证明即使采用简单的大模型应用方式，也能在这些任务上取得具有竞争力的性能。这一发现确立了大模型作为关系型数据库机器学习的新基准，并推动该方向的进一步研究。
原文摘要 · Abstract (English)
Though large language models (LLMs) have demonstrated exceptional performance across numerous problems, their application to predictive tasks in relational databases remains largely unexplored. In this work, we address the notion that LLMs cannot yield satisfactory results on relational databases due to their interconnected tables, complex relationships, and heterogeneous data types. Using the recently introduced RelBench benchmark, we demonstrate that even a straightforward application of LLMs achieves competitive performance on these tasks. These findings establish LLMs as a promising new baseline for ML on relational databases and encourage further research in this direction.
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