4 citations · 4 across the 2 of their papers we have counts for
5 papers
RUBICON: Agentic AI for Messy Enterprise Data
Fabian Wenz, Felix Treutwein, Kai Arenja +2
Enterprise data exists in many forms, such as tables, text, maps, e-mail, and CAD models, that are access-controlled and hidden behind bespoke interfaces. Current agentic AI system…
BEAVER: An Enterprise Benchmark for Text-to-SQL
Peter Baile Chen, Devin Yang, Weiyue Li +6
Existing text-to-SQL benchmarks have largely been constructed from public databases with well-structured schemas and simplistic question-SQL pairs. While large language models (LLM…
BenchPress: A Human-in-the-Loop Annotation System for Rapid Text-to-SQL Benchmark Curation
Fabian Wenz, Omar Bouattour, Devin Yang +4
Large language models (LLMs) have been successfully applied to many tasks, including text-to-SQL generation. However, much of this work has focused on publicly available datasets,…
Mind the Data Gap: Bridging LLMs to Enterprise Data Integration
Moe Kayali, Fabian Wenz, Nesime Tatbul +1
Leading large language models (LLMs) are trained on public data. However, most of the world's data is dark data that is not publicly accessible, mainly in the form of private organ…
Making LLMs Work for Enterprise Data Tasks
ÃaÄatay Demiralp, Fabian Wenz, Peter Baile Chen +3
Large language models (LLMs) know little about enterprise database tables in the private data ecosystem, which substantially differ from web text in structure and content. As LLMs'…