mlsanityData to 7 Oct 2026

rLLM: Relational Table Learning with LLMs

arXiv 2407.20157 ↗First posted 29 Jul 2024cs.AIcited by 7

Not on the map. Why?

Abstract (arXiv)

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 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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 18 of its 25 references that are in the map. First posted on arXiv 29 Jul 2024.

Builds on

18 of 25 references.

References · 18

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  4. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  5. 2017Graph Attention Networkscited by 8,854
  6. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  7. 2023A Survey of Large Language Modelscited by 2,537
  8. 2019Heterogeneous Graph Attention Networkcited by 732
  9. 2021Revisiting Deep Learning Models for Tabular Datacited by 725
  10. 2020Heterogeneous Graph Transformercited by 581
  11. 2020TabTransformer: Tabular Data Modeling Using Contextual Embeddingscited by 398
  12. 2023Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learningcited by 132
6 more
  1. 2024Let Your Graph Do the Talking: Encoding Structured Data for LLMscited by 112
  2. 2023Label-free Node Classification on Graphs with Large Language Models (LLMS)cited by 72
  3. 2023Relational Deep Learning: Graph Representation Learning on Relational Databasescited by 55
  4. 2023ConGraT: Self-Supervised Contrastive Pretraining for Joint Graph and Text Embeddingscited by 32
  5. 2020Network Embedding with Completely-imbalanced Labelscited by 21
  6. 2023From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisitedcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 5.

7 papers.

  1. 2025Large Language Models are Good Relational Learnerscited by 22
  2. 2024Tackling prediction tasks in relational databases with LLMscited by 14
  3. 2026LakeMLB: Data Lake Machine Learning Benchmarkcited by 2
  4. 2026Learning Adaptive Parallel Execution for Efficient Code Localizationcited by 0
  5. 2026Semantic Triplet Restoration: A Novel Protocol for Hierarchical Table Understanding in Large Language Modelscited by 0
  6. 2026InRTL: Effective Intra-Inter Interaction Learning for Relational Tablescited by 0
  7. 2026MetaRTL: Meta-path Attention Enhanced Relational Table Learningcited by 0