On Localization in Language Models
Explore the intriguing concept of localization in language models through this insightful one-hour lecture by Yonatan Belinkov from the Technion - Israel Institute of Technology. Delve into the long-standing debate between distributive and localist approaches in artificial intelligence and cognitive science. Examine the role of distributed representations in modern neural models for natural language processing and investigate how the scaling of models to billions of parameters impacts our understanding of information encoding. Learn about recent research on identifying and characterizing individual components like neurons and attention heads in language models. Discover how analyzing the internal structure and mechanisms of these models can shed light on their behavior in various scenarios, including memorization, gender bias, and factual recall. Gain valuable insights into how such analyses can inform mitigation strategies to enhance the robustness and currency of language models.