Peter Hase

Peter Hase, a fifth-year PhD student at the University of North Carolina NLP lab, dives into the fascinating world of large language models. He discusses the vital role of interpretability in AI, exploring how knowledge is stored and accessed. The conversation shifts to model editing, emphasizing the challenges of deleting sensitive information while maintaining data integrity. Hase also highlights the risks of easy-to-hard generalization in releasing open-source models and the impact of instructional prompts on model performance. This insightful dialogue unravels complexities in AI decision-making.

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.