Towards Benchmarking Foundation Models for Tabular Data With Text
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911049403334656 |
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| author | Mráz, Martin Das, Breenda Gupta, Anshul Purucker, Lennart Hutter, Frank |
| author_facet | Mráz, Martin Das, Breenda Gupta, Anshul Purucker, Lennart Hutter, Frank |
| contents | Foundation models for tabular data are rapidly evolving, with increasing interest in extending them to support additional modalities such as free-text features. However, existing benchmarks for tabular data rarely include textual columns, and identifying real-world tabular datasets with semantically rich text features is non-trivial. We propose a series of simple yet effective ablation-style strategies for incorporating text into conventional tabular pipelines. Moreover, we benchmark how state-of-the-art tabular foundation models can handle textual data by manually curating a collection of real-world tabular datasets with meaningful textual features. Our study is an important step towards improving benchmarking of foundation models for tabular data with text. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_07829 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Benchmarking Foundation Models for Tabular Data With Text Mráz, Martin Das, Breenda Gupta, Anshul Purucker, Lennart Hutter, Frank Machine Learning Foundation models for tabular data are rapidly evolving, with increasing interest in extending them to support additional modalities such as free-text features. However, existing benchmarks for tabular data rarely include textual columns, and identifying real-world tabular datasets with semantically rich text features is non-trivial. We propose a series of simple yet effective ablation-style strategies for incorporating text into conventional tabular pipelines. Moreover, we benchmark how state-of-the-art tabular foundation models can handle textual data by manually curating a collection of real-world tabular datasets with meaningful textual features. Our study is an important step towards improving benchmarking of foundation models for tabular data with text. |
| title | Towards Benchmarking Foundation Models for Tabular Data With Text |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.07829 |