activity
20222026
most citedUnveiling Challenges for LLMs in Enterprise Data Engineering

4 citations · 4 across the 4 of their papers we have counts for

collaborators

5 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★ 4 cited

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…

cs.DB2023

Towards Foundation Models for Relational Databases [Vision Paper]

Liane Vogel, Benjamin Hilprecht, Carsten Binnig

Tabular representation learning has recently gained a lot of attention. However, existing approaches only learn a representation from a single table, and thus ignore the potential…

cs.CL2022

Investigating User Radicalization: A Novel Dataset for Identifying Fine-Grained Temporal Shifts in Opinion

Flora Sakketou, Allison Lahnala, Liane Vogel +1

There is an increasing need for the ability to model fine-grained opinion shifts of social media users, as concerns about the potential polarizing social effects increase. However,…