rLLM: Relational Table Learning with LLMs
<|reference_start|>rLLM: Relational Table Learning with LLMs: 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.<|reference_end|>
@article{li2024rllm:,
    title={rLLM: Relational Table Learning with LLMs},
    author={Weichen Li, Xiaotong Huang, Jianwu Zheng, Zheng Wang, Chaokun Wang, Li
  Pan, Jianhua Li},
    journal={arXiv preprint arXiv:2407.20157},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.20157},
    primaryClass={cs.AI}
}