News & Events

Surgery on LLMs: Editing, Unlearning, and Securing Knowledge

Wengpeng Yin

Pennsylvania State University

Humanities Center, Conference Room D

LLMs are rapidly being deployed in high-stakes settings, from scientific discovery to legal assistance to personal health, where we can no longer treat them as static black boxes. We need ways to update, correct, and safeguard their knowledge as the world changes and as privacy risks grow. This talk will present a vision for LLM surgery: systematic ways to make models learn, forget, and protect in a controlled manner. We will explore how to edit models so they gain new knowledge without losing old capabilities, how to make them forget harmful or outdated information without collapsing, and how to truly safeguard private data rather than relying on superficial protections. Along the way, I will share surprising failure cases, new evaluation tools, and early solutions that point toward a future where LLMs can safely and reliably evolve with human needs.

Dr. Wengpeng Yin is a tenure-track Assistant Professor in the Department of Computer Science and Engineering, an affiliated faculty member in the Center for Socially Responsible Artificial Intelligence, and an Associate in the Institute for Computational and Data Sciences at Penn State, where he leads the AI4Research lab.