rLLM: Relational Table Learning with LLMs

Weichen Li, Xiaotong Huang, Jianwu Zheng, Zheng Wang, Chaokun Wang, Li Pan, Jianhua Li

2024-07-29Node Classification Classification

Abstract

We introduce rLLM (relationLLM), a PyTorch library designed for Relational Table Learning (RTL) with Large Language Models (LLMs). The core idea is to decompose state-of-the-art Graph Neural Networks, LLMs, and Table Neural Networks into standardized modules, to enable the fast construction of novel RTL-type models in a simple "combine, align, and co-train" manner. To illustrate the usage of rLLM, we introduce a simple RTL method named \textbf{BRIDGE}. Additionally, we present three novel relational tabular datasets (TML1M, TLF2K, and TACM12K) by enhancing classic datasets. We hope rLLM can serve as a useful and easy-to-use development framework for RTL-related tasks. Our code is available at: https://github.com/rllm-project/rllm.

Results

Task	Dataset	Metric	Value	Model
Classification	TACM12K	Accuracy	25.6	BRIDGE
Classification	TML1M	Accuracy	36.2	BRIDGE
Classification	TLF2K	Accuracy	42.2	BRIDGE

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