Improving Artificial Intelligence Using Multilinguality

Kenton Murray
Johns Hopkins University & George Mason University

Time: 2026-08-28, 11:00 - 12:00 ET
Location: Rice 540 and Zoom

Abstract Artificial Intelligence, Large Language Models, and Natural Language Processing are overly focused on English-only and English-centric models. Fortunately, there has been a growing interest in making models more multilingual. Yet, whereas most researchers in this field are focused on broadening coverage of people and cultures, my interests are two-fold: both expanding access, but also making core machine learning improvements that translate back to monolingual models. By focusing on other languages, we are able to design more robust methods and create novel algorithms that drive advances across all of aspects of Artificial Intelligence and Machine Learning, not just multilingual applications. In this talk, I will cover improvements my students and I have made throughout all parts of an LLM pipeline, from data curation, to pretraining, post-training, evaluation, and inference. We show how this can result in faster training time, less GPU memory usage, and fewer parameters, as well as many other advancements. While these methods were developed with a focus on multilinguality, they have been applied to improve monolingual, English-only models as well.

Bio: Kenton Murray is an Assistant Professor at George Mason University where he directs the Maieutic Lab. Previously, he was a Senior Research Scientist at Johns Hopkins University. His work focuses on Multilingual Natural Language Processing and Machine Translation with a particular emphasis on low-resource and morphologically rich languages across text, speech, and vision modalities. He received his PhD from the University of Notre Dame, a Master’s from Carnegie Mellon University, and a Bachelor’s from Princeton University.