1 citations · 1 across the 4 of their papers we have counts for
6 papers
Streaming Model Cascades for Semantic SQL
PaweÅ Liskowski, Kyle Schmaus
Modern data warehouses extend SQL with semantic operators that invoke large language models on each qualifying row, making per-row inference orders of magnitude more expensive than…
Larch: Learned Query Optimization for Semantic Predicates
Fuheng Zhao, Pawel Liskowski, Zihan Li +5
With the advent of Large Language Models (LLMs), many database systems introduced semantic operators that enabled analytical queries over unstructured data (e.g. text, images, vide…
AvalancheBench: Evaluating Enterprise Data Agents Through Latent World Recovery
Darek Kleczek, Fuheng Zhao, Alexander W. Lee +4
We introduce AvalancheBench, a benchmark for evaluating enterprise data agents through \emph{latent world recovery}. AvalancheBench improves on existing benchmarks in three ways. F…
Cortex AISQL: A Production SQL Engine for Unstructured Data
PaweÅ Liskowski, Benjamin Han, Paritosh Aggarwal +11
Snowflake's Cortex AISQL is a production SQL engine that integrates native semantic operations directly into SQL. This integration allows users to write declarative queries that co…
Large-Scale Aspect-Based Sentiment Analysis with Reasoning-Infused LLMs
PaweÅ Liskowski, Krzysztof Jankowski
We introduce Arctic-ABSA, a collection of powerful models for real-life aspect-based sentiment analysis (ABSA). Our models are tailored to commercial needs, trained on a large corp…
Arctic-TILT. Business Document Understanding at Sub-Billion Scale
Åukasz Borchmann, MichaÅ Pietruszka, Wojciech JaÅkowski +13
The vast portion of workloads employing LLMs involves answering questions grounded on PDF or scan content. We introduce the Arctic-TILT achieving accuracy on par with models 1000$\…