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Compositional Online Learning for Semantic Data Processing Systems
Paweł Liskowski, Fuheng Zhao, Benjamin Han +2
An LLM call in a semantic data processing system is expensive enough to dominate query cost, yet slow enough to hide a CPU-side learner's update behind its round-trip. In productio…
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 Agent-Driven Semantic Query Processing Through Latent World Recovery
Darek Kleczek, Fuheng Zhao, Alexander W. Lee +4
As LLMs improve, data agents are emerging as a new interface for analytics, while semantic query processing engines use LLMs to operate over structured and unstructured data. Exist…
Streaming Model Cascades for Semantic SQL
Paweł 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…
Large-Scale Aspect-Based Sentiment Analysis with Reasoning-Infused LLMs
Paweł 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…