Challenges in Augmenting Large Language Models with Private Data

Explore the challenges of integrating private data into large language models in this 58-minute Google TechTalk presented by Ashwinee Panda from Princeton University. Delve into the concept of "neural phishing," a new data extraction attack that enables adversaries to target and extract personally identifiable information (PII) from models trained on user data. Learn about Differentially Private In-Context Learning, a framework proposed to coordinate independent LLM agents for answering user queries under differential privacy. Examine methods for obtaining consensus across potentially disagreeing LLM agents and investigate the privacy-utility tradeoff of different differential privacy mechanisms. Gain insights into the ongoing developments in LLM technology and their implications for both stronger adversaries and more robust systems.

Challenges in Augmenting Large Language Models with Private Data