titleTabEmbed: Benchmarking and Learning Generalist Embeddings for Tabular Understanding
authorsMinjie Qiang, Mingming Zhang, Xiaoyi Bao, Xing Fu, Yu Cheng, Weiqiang Wang, Zhongqing Wang, Ningtao Wang
date2026-05-06
venuearXiv (preprint, not peer reviewed)
idarXiv:2605.04962
affiliationAI4Data
upvotes6 (Hugging Face Daily Papers, 2026-11-01)
linksabs pdf hf code
abstractFoundation 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. Code and datasets are publicly available at https://github.com/qiangminjie27/TabEmbed and https://huggingface.co/datasets/qiangminjie27/TabBench.

cite

@misc{qiang2026tabembed,
  title={TabEmbed: Benchmarking and Learning Generalist Embeddings for Tabular Understanding},
  author={Minjie Qiang and Mingming Zhang and Xiaoyi Bao and Xing Fu and Yu Cheng and Weiqiang Wang and Zhongqing Wang and Ningtao Wang},
  year={2026},
  eprint={2605.04962},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2605.04962}
}