Foundational machine learning research

Foundational Machine Learning Research is the dedicated core Machine Learning research division of Thomson Reuters. We are focused on research and development, with a particular focus on advanced algorithms and training techniques for Large Language Models (LLMs). Using a unique combination of algorithms across the entire training spectrum, we build the world’s best legal Language Models.

We work collaboratively with TR Labs (TR’s applied research division), academic partners at world-leading research institutions and domain experts with decades of experience. We experiment, prototype, test, and deliver ideas in the pursuit of smarter and more valuable models trained on an unprecedented wealth of data and powered by state-of-the-art technical infrastructure. Through our unique institutional experience, we have access to an unprecedented number of subject matter experts involved in data collection, testing and evaluation of trained models.

Date: December 10–15, 2024
Location: Vancouver, Canada
Event details: NeuraIPS event information

We are presenting five conference papers co-authored by Foundational Research team members as well as organizing the workshop on “Compositional Learning: Perspectives, Methods, and Paths Forward”. Come meet us at our sponsorship booth #49 and ask for our career opportunities.