TEmBed-T: A Multi-Dimensional Benchmark for Table-Level Embeddings

• URL: http://arxiv.org/abs/2607.24130v1
• Date: Mon, 27 Jul 2026 08:13:52 GMT
• Title: TEmBed-T: A Multi-Dimensional Benchmark for Table-Level Embeddings
• Abstract summary: 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.
• Score: 14.025347839787123
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tabular data is the dominant structured-data modality, and learning table representations has become a core research direction. Table-level embeddings in particular underpin a wide range of applications, including table retrieval, data lake discovery, and table classification. Despite their importance, there is still limited understanding of how different embedding approaches behave across tasks, making systematic evaluation and analysis essential. In this work, we introduce a systematic evaluation of table-level embeddings that captures several complementary properties required for downstream effectiveness. We realize this evaluation by extending TEmBed, a recently proposed testbed for tabular embeddings, whose table-level coverage is currently limited to a single retrieval task. An empirical study over the TEmBed model pool confirms that no single model excels across all tasks, demonstrating that table-level embedding quality cannot be reduced to retrieval alone.

Related papers

• 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)
• FollowTable: A Benchmark for Instruction-Following Table Retrieval [30.435522584995336]
  We formalize Instruction-Following Table Retrieval (IFTR), a new task that requires models to jointly satisfy topical relevance and fine-grained instruction constraints.<n>We introduce FollowTable, the first large-scale benchmark for IFTR, constructed via a taxonomy-driven annotation pipeline.
  arXiv  Detail & Related papers  (2026-05-01T04:42:13Z)
• Towards Universal Tabular Embeddings: A Benchmark Across Data Tasks [15.117602507724726]
  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.
  arXiv  Detail & Related papers  (2026-04-23T14:05:38Z)
• TReB: A Comprehensive Benchmark for Evaluating Table Reasoning Capabilities of Large Language Models [30.26407735827857]
  Reasoning with table-structured data poses significant challenges for large language models (LLMs)<n>We present a comprehensive table reasoning evolution benchmark, TReB, which measures both shallow table understanding abilities and deep table reasoning abilities.<n>We create an evaluation framework to robustly measure table reasoning capabilities with three distinct inference modes, TCoT, PoT and ICoT.
  arXiv  Detail & Related papers  (2025-06-23T09:02:04Z)
• RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarking [63.253294691180635]
  In real-world scenarios, beyond pure text, a substantial amount of knowledge is stored in tables.<n>We first propose a table-corpora-aware RAG framework, named T-RAG, which consists of the hierarchical memory index, multi-stage retrieval, and graph-aware prompting.
  arXiv  Detail & Related papers  (2025-04-02T04:24:41Z)
• MT-RAIG: Novel Benchmark and Evaluation Framework for Retrieval-Augmented Insight Generation over Multiple Tables [11.268174270952489]
  MT-RAIG Bench is designed to evaluate systems on Retrieval-Augmented Insight Generation over Mulitple-Tables.<n>We introduce a fine-grained evaluation framework MT-RAIG Eval, which better alignment with human quality judgments on the generated insights.
  arXiv  Detail & Related papers  (2025-02-17T12:21:13Z)
• 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)
• Tab.IAIS: Flexible Table Recognition and Semantic Interpretation System [84.39812458417246]
  We develop two rule-based algorithms that perform the complete table recognition process and support the most frequent table formats. To incorporate the extraction of semantic information into the table recognition process, we develop a graph-based table interpretation method. Our table recognition approach achieves results competitive with state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-05-25T12:31:02Z)

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.