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Expo Talk Panel
Vowpal Wabbit: year in review and looking ahead in an LLM world
John Langford · Byron Xu · Cheng Tan · Jack Gerrits · Lili Wu · Mark Rucker · Olga Vrousgou

Sun Jul 23 08:00 PM -- 09:00 PM (PDT) @ Theater 320 Emaline

With the runaway success of Large Language Models, related applications are being rapidly built and deployed. LLM Application developers are faced with some common issues around learning from feedback, prompt selection, response selection, order of prompts, limited context lengths, slow response times.

Reinforcement learning is a promising technology to address these challenges, particularly with the advent of sample-efficient algorithms for personalization and optimization scenarios. At the forefront of these RL solutions is Vowpal Wabbit (VW), an open-source machine learning toolkit and research platform. VW is known for its speed and scalability and continues to grow through the introduction of cutting-edge algorithms.

In the past year we have been working on integrating Vowpal Wabbit with Large Language Model applications, to address some of the aformentioned challenges.

This workshop aims to explore this integration, we'll discuss the following key areas:

  • A brief introduction to Vowpal Wabbit and its use of Contextual Bandits learning.
  • Strategies to harness the full potential of Vowpal Wabbit in various application scenarios.
  • Learned Orchestration with Vowpal Wabbit. Considering the rapid growth of LLMs, there's a promising future in combining these models with VW.

Join us as we delve into this forward-looking exploration. Whether you're a data scientist or a machine learning enthusiast, this workshop will offer valuable insights into building VW and LLM based applications and how it could impact your machine learning applications.

Author Information

John Langford (Microsoft Research)
Byron Xu (Microsoft Research)
Cheng Tan (Microsoft)
Jack Gerrits (Microsoft)
Lili Wu (Microsoft)
Mark Rucker
Olga Vrousgou (Microsoft Research)

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