Abstract
In this talk, Igor will share the latest findings from his research group on the development, evaluation, and deployment of large language models (LLMs) and their multi-modal extensions. We will outline the end-to-end pipeline, with a particular focus on the challenges and costs associated with data generation in the later stages of training. Additionally, we will explore strategies for integrating multiple models into inference systems to enhance efficiency. Finally, we will highlight key tasks where even the most advanced multi-modal models face difficulties and discuss approaches that yield significantly better performance.
Bio
Igor is an Assistant Professor at the University of Hawai’i at Manoa. In 2022, I graduated with a Ph.D. in Engineering from UC Berkeley and worked as a research engineer at Google DeepMind and Meta AI. He specializes in large language models (LLM), focusing on the efficient development and robust evaluation of computer systems that automate economically viable yet tedious tasks typically requiring human intervention. His immediate areas of interest include: