Challenges of Achieving Interpretability in Transformative Models

The chapter explores the difficulties in achieving interpretability in models like GPT-4 and GPT-2, discussing the limitations of a proposed metric for measuring loss recovery and the importance of mechanistic interpretability. They also delve into the threshold for model differences and safety concerns in explanations.