arXiv AI

MetaRTL: Meta-path Attention Enhanced Relational Table Learning

MetaRTL is a two-stage framework for relational table learning that first generates lightweight pre-trained table embeddings and then applies non‑parametric message passing to extract meta‑path features. These features are aggregated using an attention module called MetaAttn, shifting computation from deep GNN stacks to efficient meta‑path aggregation. Experiments on 10 real‑world datasets across 24 tasks show that MetaRTL achieves strong performance while reducing computational cost.

arXiv Machine Learning

Aug 27

MetaSieve: Faster Relational Deep Learning through SQL-Based Metapath Selection

MetaSieve is a metapath selection layer that reduces subgraph size in relational deep learning by pruning uninformative metapaths using SQL join and aggregation statistics. It scores candidate metapath extensions with a lightweight function that favors informative yet lightweight paths, discarding those below a threshold. The method is independent of GNN parameters and, when applied to the RelBench benchmark, consistently cuts per‑epoch training time while preserving or improving accuracy.

By Fahim Shahriar Khan, Ashraf Aboulnaga

benchmarks data-engineering

arXiv Machine Learning

Sep 14

InRTL: Effective Intra-Inter Interaction Learning for Relational Tables

InRTL: Effective Intra-Inter Interaction Learning for Relational Tables proposes a unified framework that explicitly models dependencies both within and across relational tables. The approach introduces intra-table interactions to capture associations among rows in the same table and inter-table interactions to capture dependencies between rows linked by primary key–foreign key relationships. It employs a column-aware table encoder, Transformer-based self-attention and cross-attention modules, and incorporates linearized attention and heterogeneous graph neural networks to improve scalability, demonstrating effectiveness across ten datasets and 24 real-world tasks.

By Weichen Li, Ken Zhong, Zheng Wang, Li Pan, Jianhua Li

llms

arXiv Machine Learning

Sep 2

Relational Task Generation Language: A Declarative Specification Framework for Relational Deep Learning

arXiv:2609.01292v1 Announce Type: cross Abstract: Relational Deep Learning (RDL) has become a powerful paradigm for learning from multi-tabular data. However, manually defining RDL prediction tasks i...

By Oleksii Kolesnichenko, Jakub Pele\v{s}ka, Gustav \v{S}\'{\i}r

benchmarks data-engineering

arXiv AI

Jul 7

A Fair Benchmarking of Deep Relational Database Learning Models

arXiv:2607. 03659v1 Announce Type: cross Abstract: Relational databases (RDBs) are the primary data infrastructure in many enterprises, yet recent deep learning methods designed for RDBs have been evaluated under inconsistent experimental protocols, making fair comparison difficult.

By Kazi F. Akhter, Bharath Ajendla, Manar D. Samad

llms benchmarks

arXiv AI

Jun 30

Latent Bridges for Multi-Table Question Answering

arXiv:2606. 28916v1 Announce Type: cross Abstract: We introduce GRAB, a constructor-encoder-bridge pipeline for table question answering.

By Simone Varriale, Tamara Cucumides, Floris Geerts, Paolo Papotti

llms nlp

arXiv AI

Jun 9

What Makes a Desired Graph for Relational Deep Learning?

arXiv:2606. 08491v1 Announce Type: new Abstract: Relational deep learning (RDL) converts relational databases (RDBs) into heterogeneous graphs, but graphs derived directly from database schemas are often not well suited for how graph neural networks (GNNs) perform relational reasoning.

By Yao Cheng, Siqiang Luo

safety

arXiv AI

Jun 3

RelGT-AC: A Relational Graph Transformer for Autocomplete Tasks in Relational Databases

arXiv:2606. 03040v1 Announce Type: new Abstract: Relational databases underpin modern enterprise, scientific, and healthcare systems, yet predictive machine learning on such data remains challenging due to their multi-table, heterogeneous, and temporal structure.

By Phillip Jiang

llms

Hugging Face Trending Papers

Sep 1

H2Table: Hierarchical Hypergraph-Enhanced Large Language Models for Complex Table Reasoning

Tables are ubiquitous across diverse domains, yet reasoning over them remains a significant challenge for modern large language models (LLMs). Current approaches typically linearize tables into sequen...

llms rag nlp benchmarks

arXiv AI

Sep 2

H2Table: Hierarchical Hypergraph-Enhanced Large Language Models for Complex Table Reasoning

H2Table introduces a hierarchical hypergraph representation for complex tables, enabling a hypergraph encoder to capture semantic relationships between headers and cells. The framework uses learnable query vectors to extract structural embeddings for large language models. Experiments on the HiTab dataset show a 22.88% improvement over state‑of‑the‑art baselines on tables with four levels of nesting.

By Jia Ling, Yangfan Wang, Chen Tang, Haoming Tan, Yang Yang, Yi Guan, Jingchi Jiang

llms rag nlp benchmarks

arXiv Machine Learning

Sep 23

Discovery-Driven Integration of Disjoint Tables via Text

arXiv:2609.26658v1 Announce Type: cross Abstract: Integrating heterogeneous datasets within data lakes is a critical challenge, particularly for semantically related tables that lack the explicit att...

By Md Ataur Rahman, Dimitris Sacharidis, Oscar Romero, Sergi Nadal

llms multimodal benchmarks

arXiv Machine Learning

Aug 14

Incremental Evaluation and Training in Relational Deep Learning

arXiv:2608. 13023v1 Announce Type: new Abstract: Relational Deep Learning (RDL) models multi-tabular databases as temporal heterogeneous graphs to enable end-to-end representation learning.

By Jakub Pele\v{s}ka, Gustav \v{S}\'ir

fine-tuning benchmarks

arXiv AI

Sep 1

CORE-T: COherent REtrieval of Tables for Text-to-SQL

arXiv:2601.13111v3 Announce Type: replace-cross Abstract: Realistic text-to-SQL workflows often require joining multiple tables. As a result, accurately retrieving the relevant set of tables becomes...

By Hassan Soliman, Vivek Gupta, Dan Roth, Iryna Gurevych

llms data-engineering

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