rLLM: Relational Table Learning with LLMs

TL;DR

rLLM is a flexible PyTorch library that simplifies the development of relational table learning models using LLMs, enabling rapid model construction and introducing new datasets.

Contribution

The paper presents rLLM, a modular framework for RTL with LLMs, and introduces a new RTL method BRIDGE along with three novel relational datasets.

Findings

rLLM facilitates quick development of RTL models.

BRIDGE demonstrates effectiveness in relational table learning.

New datasets enhance benchmarking for RTL tasks.

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.