Stanford CS25: Transformers United V6 I Distinct Modes of Generalization from Parameters and Context

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education May 7, 2026 This seminar covers: • Two methods for teaching information to language models: training (updating parameters) or in-context learning (providing information in prompts) • Striking differences in the types of generalization that models make when they learn information via these two routes • Three different strategies that can help bridge the gap, based on data augmentation, retrieval, and RL Follow along with the seminar schedule. Visit: https://web.stanford.edu/class/cs25/ Guest Speaker: Andrew Lampinen (Anthropic) Instructors: • Steven Feng, Stanford Computer Science PhD student and NSERC PGS-D scholar • Karan P. Singh, Electrical Engineering PhD student and NSF Graduate Research Fellow in the Stanford Translational AI Lab • Michael C. Frank, Benjamin Scott Crocker Professor of Human Biology Director, Symbolic Systems Program • Christopher Manning, Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science, Co-Founder and Senior Fellow of the Stanford Institute for Human-Centered Artificial Intelligence (HAI)