rLLM (Documentation|Paper)

Note: we will release our first version (v0.1) at August 11, 2024.

rLLM (relationLLM) is an easy-to-use Pytorch library for Relational Table Learning (RTL) with LLMs, by performing two key functions:

1. Breaks down state-of-the-art GNNs, LLMs, and TNNs as standardized modules.
2. Facilitates novel model building in a "combine, align, and co-train" way using these modules.

How to Try:

Let's run an RTL-type method BRIDGE as an example:

# cd ./examples
# set parameters if necessary

python bridge/bridge_tacm12k.py
python bridge/bridge_tlf2k.py
python bridge/bridge_tml1m.py

Highlight Features:

• LLM-friendly: Modular interface designed for LLM-oriented applications, integrating smoothly with LangChain and Hugging Face transformers.
• One-Fit-All Potential: Processes various graphs (like Social/Citation/E-commerce Networks) by treating them as multiple tables linked by foreigner keys.
• Novel Datasets: Introduces three new relational table datasets useful for RTL model design. Includes the standard classification task, with examples.
• Community Support: Maintained by students and teachers from Shanghai Jiao Tong University and Tsinghua University. Supports the SJTU undergraduate course "Content Understanding (NIS4301)" and the graduate course "Social Network Analysis (NIS8023)".

Citation

@article{rllm2024,
      title={rLLM: Relational Table Learning with LLMs}, 
      author={Weichen Li and Xiaotong Huang and Jianwu Zheng and Zheng Wang and Chaokun Wang and Li Pan and Jianhua Li},
      year={2024},
      eprint={2407.20157},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2407.20157}, 
}