Towards Universal Tabular Embeddings: A Benchmark Across Data Tasks

2026-04-23

AI Summary

Existing table embedding methods lack a unified benchmark, making effective cross-task and cross-domain comparisons challenging. To address this gap, this work proposes TEmBed—the first comprehensive evaluation framework for table embeddings that systematically covers four representation granularities: cell, row, column, and full-table levels. Under a consistent experimental setup, the study conducts a systematic assessment of multiple representative models across diverse tasks and domains. The empirical results reveal significant performance variations among existing approaches at different granularities and tasks, offering practical guidance for model selection in real-world applications and advancing the development of general-purpose table representation learning.

Technology Category

• Machine Learning: Representation Learning
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Knowledge Representation and Reasoning: Argumentation

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Tabular foundation models aim to learn universal representations of tabular data that transfer across tasks and domains, enabling applications such as table retrieval, semantic search and table-based prediction. Despite the growing number of such models, it remains unclear which approach works best in practice, as existing methods are often evaluated under task-specific settings that make direct comparison difficult. To address this, we introduce TEmBed, the Tabular Embedding Test Bed, a comprehensive benchmark for systematically evaluating tabular embeddings across four representation levels: cell, row, column, and table. Evaluating a diverse set of tabular representation learning models, we show that which model to use depends on the task and representation level. Our results offer practical guidance for selecting tabular embeddings in real-world applications and lay the groundwork for developing more general-purpose tabular representation models.

Problem

• tabular embeddings
• foundation models
• benchmark
• representation learning
• table retrieval

Innovation

• tabular embeddings
• foundation models
• benchmark
• representation learning
• TEmBed