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rLLM: Relational Table Learning with LLMs

29 Jul 2024arXiv:2407.20157archive 2025-07-28

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

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.

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Code

rllm-project/rllm_datasets officialmentioned on GitHub report
rllm-project/rllm mentioned in papermentioned on GitHubpytorch report

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ClassificationNode Classification

Datasets

Introduced by this paper, per the archive.

TACM12KTLF2KTML1M

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Classification TACM12K BRIDGE Accuracy 25.6 #1 of 1 Archive leaderboard report
Classification TLF2K BRIDGE Accuracy 42.2 #1 of 1 Archive leaderboard report
Classification TML1M BRIDGE Accuracy 36.2 #1 of 1 Archive leaderboard report

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