Peter Hase, a PhD student, discusses scalable oversight in neural networks, knowledge localization in LLMs, and the importance of deleting sensitive information. They explore interpretability techniques, surgical model editing, and task specification in pre-trained models, highlighting challenges in updating model knowledge and defending against information extraction.

In this discussion, Peter Hase, a researcher specializing in large language models, dives into the intriguing world of AI beliefs. He explores whether LLMs truly have beliefs and how to detect and edit them. A key focus is on the complexities of interpreting neural representations and the implications of belief localization. The conversation also covers the concept of easy-to-hard generalization, revealing insights on how AI tackles different task difficulties. Join Peter as he navigates these thought-provoking topics, blending philosophy with practical AI research.