TEmBed-T: A Multi-Dimensional Benchmark for Table-Level Embeddings
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 t...
| Authors | Ayeen Poostforoushan, Liane Vogel, Carsten Binnig |
|---|---|
| Primary category | cs.DB |
| Published | 27 July 2026 |
| Last updated at source | 27 July 2026 |
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