221 citations · 276 across the 13 of their papers we have counts for
5 papers · 1 filter
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'…
CascadeServe: Unlocking Model Cascades for Inference Serving
Ferdi Kossmann, Ziniu Wu, Alex Turk +3
Machine learning (ML) models are increasingly deployed to production, calling for efficient inference serving systems. Efficient inference serving is complicated by two challenges:…
Kairos: Efficient Temporal Graph Analytics on a Single Machine
Joana M. F. da Trindade, Julian Shun, Samuel Madden +1
Many important societal problems are naturally modeled as algorithms over temporal graphs. To date, however, most graph processing systems remain inefficient as they rely on distri…