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Challenges and Opportunities of Table Representation Learning (Dagstuhl Seminar 25182)

Authors: Carsten Binnig, Julian Martin Eisenschlos, Madelon Hulsebos, and Frank Hutter

Published in: Dagstuhl Reports, Volume 15, Issue 4 (2025)

Abstract

The growing volume and importance of structured data have sparked increasing interest in Table Representation Learning (TRL), an emerging field that leverages neural models to learn abstract, general-purpose representations for tabular data to support a wide range of downstream tasks such as tabular prediction, table question answering, tabular data cleaning, and many more. This seminar gathered the different communities (ML, NLP, IR, DB) who work on this topic to discuss the challenges & long-term vision of this field. From the organizers: Carsten Binnig, Julian Eisenschlos, Madelon Hulsebos, Frank Hutter.

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Carsten Binnig, Julian Martin Eisenschlos, Madelon Hulsebos, and Frank Hutter. Challenges and Opportunities of Table Representation Learning (Dagstuhl Seminar 25182). In Dagstuhl Reports, Volume 15, Issue 4, pp. 126-138, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@Article{binnig_et_al:DagRep.15.4.126,
  author =	{Binnig, Carsten and Eisenschlos, Julian Martin and Hulsebos, Madelon and Hutter, Frank},
  title =	{{Challenges and Opportunities of Table Representation Learning (Dagstuhl Seminar 25182)}},
  pages =	{126--138},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2025},
  volume =	{15},
  number =	{4},
  editor =	{Binnig, Carsten and Eisenschlos, Julian Martin and Hulsebos, Madelon and Hutter, Frank},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.15.4.126},
  URN =		{urn:nbn:de:0030-drops-252531},
  doi =		{10.4230/DagRep.15.4.126},
  annote =	{Keywords: applications of table representation learning, benchmarks and datasets for table representation learning, pre-trained (language) models for tables and databases, representation and generative learning for data management and analysis}
}

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Challenges and Opportunities of Table Representation Learning (Dagstuhl Seminar 25182)

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