rLLM: Relational Table Learning with LLMs
This provides a development framework for researchers working on relational table learning tasks, though it appears incremental as it builds on existing methods.
The authors introduced rLLM, a PyTorch library for Relational Table Learning with LLMs that decomposes existing models into standardized modules to enable fast construction of novel RTL models, and created three new relational tabular datasets (TML1M, TLF2K, TACM12K) by enhancing classic datasets.
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.