{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/rllm-relational-table-learning-with-llms","title":"rLLM: Relational Table Learning with LLMs","arxiv_id":"2407.20157","date":"2024-07-29","proceeding":null,"authors":["Weichen Li","Xiaotong Huang","Jianwu Zheng","Zheng Wang","Chaokun Wang","Li Pan","Jianhua Li"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2407.20157v1","url_pdf":"https://arxiv.org/pdf/2407.20157v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"rllm-relational-table-learning-with-llms","repo_url":"https://github.com/rllm-project/rllm_datasets","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"rllm-relational-table-learning-with-llms","repo_url":"https://github.com/rllm-project/rllm","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"node-classification","task_name":"Node Classification"}],"methods":[{"method_slug":null,"method_name":"Library"}],"datasets_introduced":[{"slug":"tacm12k","name":"TACM12K","full_name":"Table-ACM12K"},{"slug":"tlf2k","name":"TLF2K","full_name":"Table-LastFm2K"},{"slug":"tml1m","name":"TML1M","full_name":"Table-MovieLens1M"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/classification-on-tacm12k","task":"Classification","dataset":"TACM12K","model":"BRIDGE","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"25.6"},"uses_additional_data":false},{"leaderboard":"/sota/classification-on-tlf2k","task":"Classification","dataset":"TLF2K","model":"BRIDGE","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"42.2"},"uses_additional_data":false},{"leaderboard":"/sota/classification-on-tml1m","task":"Classification","dataset":"TML1M","model":"BRIDGE","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"36.2"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2407.20157","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}