@inproceedings{qiang-etal-2026-tabembed,
title = "{T}ab{E}mbed: Benchmarking and Learning Generalist Embeddings for Tabular Understanding",
author = "Qiang, Minjie and
Zhang, Mingming and
Bao, Xiaoyi and
Fu, Xing and
Cheng, Yu and
Wang, Weiqiang and
Wang, Zhongqing and
Wang, Ningtao",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.352/",
pages = "7355--7377",
ISBN = "979-8-89176-462-0",
abstract = "Foundation models have established unified representations for natural language processing, yet this paradigm remains largely unexplored for tabular data. Existing methods face fundamental limitations: LLM-based approaches lack retrieval-compatible vector outputs, whereas text embedding models often fail to capture tabular structure and numerical semantics. To bridge this gap, we first introduce the Tabular Embedding Benchmark (TabBench), a comprehensive suite designed to evaluate the tabular understanding capability of embedding models. We then propose TabEmbed, the first generalist embedding model that unifies tabular classification and retrieval within a shared embedding space. By reformulating diverse tabular tasks as semantic matching problems, TabEmbed leverages large-scale contrastive learning with positive-aware hard negative mining to discern fine-grained structural and numerical nuances. Experimental results on TabBench demonstrate that TabEmbed significantly outperforms state-of-the-art text embedding models, establishing a new baseline for universal tabular representation learning."
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<abstract>Foundation models have established unified representations for natural language processing, yet this paradigm remains largely unexplored for tabular data. Existing methods face fundamental limitations: LLM-based approaches lack retrieval-compatible vector outputs, whereas text embedding models often fail to capture tabular structure and numerical semantics. To bridge this gap, we first introduce the Tabular Embedding Benchmark (TabBench), a comprehensive suite designed to evaluate the tabular understanding capability of embedding models. We then propose TabEmbed, the first generalist embedding model that unifies tabular classification and retrieval within a shared embedding space. By reformulating diverse tabular tasks as semantic matching problems, TabEmbed leverages large-scale contrastive learning with positive-aware hard negative mining to discern fine-grained structural and numerical nuances. Experimental results on TabBench demonstrate that TabEmbed significantly outperforms state-of-the-art text embedding models, establishing a new baseline for universal tabular representation learning.</abstract>
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%0 Conference Proceedings
%T TabEmbed: Benchmarking and Learning Generalist Embeddings for Tabular Understanding
%A Qiang, Minjie
%A Zhang, Mingming
%A Bao, Xiaoyi
%A Fu, Xing
%A Cheng, Yu
%A Wang, Weiqiang
%A Wang, Zhongqing
%A Wang, Ningtao
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F qiang-etal-2026-tabembed
%X Foundation models have established unified representations for natural language processing, yet this paradigm remains largely unexplored for tabular data. Existing methods face fundamental limitations: LLM-based approaches lack retrieval-compatible vector outputs, whereas text embedding models often fail to capture tabular structure and numerical semantics. To bridge this gap, we first introduce the Tabular Embedding Benchmark (TabBench), a comprehensive suite designed to evaluate the tabular understanding capability of embedding models. We then propose TabEmbed, the first generalist embedding model that unifies tabular classification and retrieval within a shared embedding space. By reformulating diverse tabular tasks as semantic matching problems, TabEmbed leverages large-scale contrastive learning with positive-aware hard negative mining to discern fine-grained structural and numerical nuances. Experimental results on TabBench demonstrate that TabEmbed significantly outperforms state-of-the-art text embedding models, establishing a new baseline for universal tabular representation learning.
%U https://aclanthology.org/2026.findings-emnlp.352/
%P 7355-7377
Markdown (Informal)
[TabEmbed: Benchmarking and Learning Generalist Embeddings for Tabular Understanding](https://aclanthology.org/2026.findings-emnlp.352/) (Qiang et al., Findings 2026)
ACL
- Minjie Qiang, Mingming Zhang, Xiaoyi Bao, Xing Fu, Yu Cheng, Weiqiang Wang, Zhongqing Wang, and Ningtao Wang. 2026. TabEmbed: Benchmarking and Learning Generalist Embeddings for Tabular Understanding. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 7355–7377, Budapest, Hungary. Association for Computational Linguistics.