LLM Safety, Alignment, and Generalization

Explore a comprehensive lecture on the critical aspects of Large Language Model (LLM) safety, alignment, and generalization. Delve into the challenges of ruling out catastrophic harms as LLM capabilities rapidly improve across various domains. Understand the importance of making affirmative safety cases for LLMs and the need to comprehend their motivational structures, especially as they become capable of complex autonomous plans. Examine the necessity for developing a science of LLM generalization to understand how training data influences a model's beliefs and motivations. Learn from Roger Grosse of the University of Toronto as part of the Simons Institute's Special Year on Large Language Models and Transformers: Part 1 Boot Camp.