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.DB2026
The Stretto Execution Engine for LLM-Augmented Data Systems
Gabriele Sanmartino, Matthias Urban, Paolo Papotti +1
LLM-augmented data systems enable semantic querying over structured and unstructured data, but executing queries with LLM-powered operators introduces a fundamental runtime-accurac…
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…