1 citations · 1 across the 3 of their papers we have counts for
6 papers
From Data Querying to Data Investigations: Rethinking Natural Language Interfaces for Databases
Fabian Wenz, Zixuan Chen, Carsten Binnig
Natural language (NL) interfaces to databases have been optimized for the wrong problem. The dominant Text-to-SQL paradigm assumes that users ask questions that can be answered by…
RUBICON: Agentic AI for Messy Enterprise Data
Fabian Wenz, Felix Treutwein, Çagatay Demiralp +1
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…
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…
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…
Making LLMs Work for Enterprise Data Tasks
Çağatay 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'…