3 papers
cs.DB2026
TEmBed-T: A Multi-Dimensional Benchmark for Table-Level Embeddings
Ayeen Poostforoushan, Liane Vogel, Carsten Binnig
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…
cs.LG2026
Towards Universal Tabular Embeddings: A Benchmark Across Data Tasks
Liane Vogel, Kavitha Srinivas, Niharika D'Souza +3
Tabular foundation models aim to learn universal representations of tabular data that transfer across tasks and domains, enabling applications such as table retrieval, semantic sea…
cs.DB2025
Unveiling Challenges for LLMs in Enterprise Data Engineering
Jan-Micha Bodensohn, Ulf Brackmann, Liane Vogel +2
Large Language Models (LLMs) promise to automate data engineering on tabular data, offering enterprises a valuable opportunity to cut the high costs of manual data handling. But th…