Towards Universal Tabular Embeddings: A Benchmark Across Data Tasks

• URL: http://arxiv.org/abs/2604.21696v1
• Date: Thu, 23 Apr 2026 14:05:38 GMT
• Title: Towards Universal Tabular Embeddings: A Benchmark Across Data Tasks
• Abstract summary: Tabular representation learning models aim to learn universal representations of data that transfer across tasks and domains.<n>Existing methods are often evaluated under task-specific settings that make direct comparison difficult.<n>We introduce TEmBed, a benchmark for systematically evaluating embeddings across four representation levels.
• Score: 15.117602507724726
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• TEmBed-T: A Multi-Dimensional Benchmark for Table-Level Embeddings [14.025347839787123]
  We introduce a systematic evaluation of table-level embeddings that captures several complementary properties required for downstream effectiveness.<n>An empirical study over the TEmBed model pool confirms that no single model excels across all tasks.
  arXiv  Detail & Related papers  (2026-07-27T08:13:52Z)
• Beyond IID: How General Are Tabular Foundation Models, Really? [76.03669797389378]
  Research communities are increasingly evaluating foundation models on diverse datasets and tasks.<n>Standard benchmarks are mostly defined for tasks where tabular foundation models already excel.<n>We introduce BeyondArena, the first unified holistic benchmark for tabular data.
  arXiv  Detail & Related papers  (2026-06-29T14:55:52Z)
• MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image [52.47434184153733]
  We introduce MulTaBench, a benchmark of 40 datasets split equally between image-tabular and text-tabular tasks.<n>We show that the gains from target-aware representation tuning generalize across both text and image modalities.
  arXiv  Detail & Related papers  (2026-05-11T14:12:05Z)
• TabEmbed: Benchmarking and Learning Generalist Embeddings for Tabular Understanding [35.46103363192022]
  We introduce the Tabular Embedding Benchmark (TabBench) to evaluate the capability of embedding models.<n>We then propose TabEmbed, the first generalist embedding model that unifies tabular classification and retrieval within a shared embedding space.<n> Experimental results on TabBench demonstrate that TabEmbed significantly outperforms state-of-the-art text embedding models.
  arXiv  Detail & Related papers  (2026-05-06T14:22:34Z)
• Universal Embeddings of Tabular Data [0.0]
  Tabular data in relational databases represents a significant portion of industrial data.<n>We present a novel framework for generating universal, i.e., task-independent embeddings of tabular data for performing downstream tasks without predefined targets.
  arXiv  Detail & Related papers  (2025-07-08T11:45:29Z)
• Representation Learning for Tabular Data: A Comprehensive Survey [23.606506938919605]
  Tabular data, structured as rows and columns, is among the most prevalent data types in machine learning classification and regression applications.<n>Deep Neural Networks (DNNs) have recently demonstrated promising results through their capability of representation learning.<n>We organize existing methods into three main categories according to their generalization capabilities.
  arXiv  Detail & Related papers  (2025-04-17T17:58:23Z)
• FLEXTAF: Enhancing Table Reasoning with Flexible Tabular Formats [48.47559543509975]
  We propose FLEXTAF-Single and FLEXTAF-Vote to enhance table reasoning performance by employing flexible formats. Our experiments on WikiTableQuestions and TabFact reveal significant improvements, with average gains of 2.3% and 4.8%.
  arXiv  Detail & Related papers  (2024-08-16T17:00:11Z)
• A Closer Look at Deep Learning Methods on Tabular Datasets [78.61845513154502]
  We present an extensive study on TALENT, a collection of 300+ datasets spanning broad ranges of size.<n>Our evaluation shows that ensembling benefits both tree-based and neural approaches.
  arXiv  Detail & Related papers  (2024-07-01T04:24:07Z)
• LaTable: Towards Large Tabular Models [63.995130144110156]
  Tabular generative foundation models are hard to build due to the heterogeneous feature spaces of different datasets. LaTable is a novel diffusion model that addresses these challenges and can be trained across different datasets. We find that LaTable outperforms baselines on in-distribution generation, and that finetuning LaTable can generate out-of-distribution datasets better with fewer samples.
  arXiv  Detail & Related papers  (2024-06-25T16:03:50Z)
• QFMTS: Generating Query-Focused Summaries over Multi-Table Inputs [63.98556480088152]
  Table summarization is a crucial task aimed at condensing information into concise and comprehensible textual summaries. We propose a novel method to address these limitations by introducing query-focused multi-table summarization. Our approach, which comprises a table serialization module, a summarization controller, and a large language model, generates query-dependent table summaries tailored to users' information needs.
  arXiv  Detail & Related papers  (2024-05-08T15:05:55Z)
• TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning [55.33939289989238]
  We propose TAP4LLM as a versatile pre-processor suite for leveraging large language models (LLMs) in table-based tasks effectively. It covers several distinct components: (1) table sampling to decompose large tables into manageable sub-tables based on query semantics, (2) table augmentation to enhance tables with additional knowledge from external sources or models, and (3) table packing & serialization to convert tables into various formats suitable for LLMs' understanding.
  arXiv  Detail & Related papers  (2023-12-14T15:37:04Z)
• UniTabE: A Universal Pretraining Protocol for Tabular Foundation Model in Data Science [16.384705926693073]
  This study seeks to extend the power of pretraining methodologies to facilitate the prediction over tables in data science. We introduce UniTabE, a method designed to process tables in a uniform manner, devoid of constraints imposed by specific table structures. In order to implement the pretraining phase, we curated an expansive dataset comprising approximately 13B samples, meticulously gathered from the Kaggle platform.
  arXiv  Detail & Related papers  (2023-07-18T13:28:31Z)
• STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tables [64.0903766169603]
  We propose a framework for few-shot semi-supervised learning, coined Self-generated Tasks from UNlabeled Tables (STUNT) Our key idea is to self-generate diverse few-shot tasks by treating randomly chosen columns as a target label. We then employ a meta-learning scheme to learn generalizable knowledge with the constructed tasks.
  arXiv  Detail & Related papers  (2023-03-02T02:37:54Z)
• TABBIE: Pretrained Representations of Tabular Data [22.444607481407633]
  We devise a simple pretraining objective that learns exclusively from tabular data. Unlike competing approaches, our model (TABBIE) provides embeddings of all table substructures. A qualitative analysis of our model's learned cell, column, and row representations shows that it understands complex table semantics and numerical trends.
  arXiv  Detail & Related papers  (2021-05-06T11:15:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.