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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.20157 |
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| _version_ | 1866918197175779328 |
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| author | Li, Weichen Huang, Xiaotong Zheng, Jianwu Wang, Zheng Wang, Chaokun Pan, Li Li, Jianhua |
| author_facet | Li, Weichen Huang, Xiaotong Zheng, Jianwu Wang, Zheng Wang, Chaokun Pan, Li Li, Jianhua |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_20157 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | rLLM: Relational Table Learning with LLMs Li, Weichen Huang, Xiaotong Zheng, Jianwu Wang, Zheng Wang, Chaokun Pan, Li Li, Jianhua Artificial Intelligence 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. |
| title | rLLM: Relational Table Learning with LLMs |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2407.20157 |