Deploying LLMs on Structured Data Tasks: Lessons from the Trenches // Laurel Orr // LLMs III Talk

// Abstract
Join us for an introduction to NSQL, a new family of open-source foundation models with up to 7B parameters automating SQL generation tasks. We will explore the limitations of existing open and closed-source foundation models for enterprise use, including issues of customization, quality, and privacy. We will highlight how NSQL addresses these challenges with its open-source nature, specialized training for SQL tasks, and a range of model sizes to accommodate diverse hardware configurations. Included in the talk will be NSQL's data generation process and GPU training approach, underlining its advantages over other foundation models for SQL generation. We will demonstrate how the NSQL models outperform existing open source models for SQL generation and, by starting from the newest LLama2 commercially available model, we even beat closed source models.
// Bio
Laurel Orr is a researcher at @NumbersStation working applying generative AI to data tasks. She graduated with a PhD in Databases and Data Management from Paul G Allen School for Computer Science and Engineering at the University of Washington and then was a PostDoc at Stanford working for Chris Ré in the HazyReserach Labs. Her research interests are broadly at the intersection of artifical intelligence, foundation models, and data management. She focuses on how to train, customize, and deploy foundation models to data tasks such as data cleaning, record matching, and generating code snippets for determinisitic data transformations. This includes problems around data curation for training, efficient model training and inference for batch workloads, and prompting paradigms for high performant, personalized models.
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