Enabling Personalized On-Device Large Language Model Adaptation through Self-Supervised Data Selection and Synthesis

A novel framework to enable on-device personalization of large language models by selecting and storing the most representative data in a self-supervised manner, and generating additional semantically similar data to enhance fine-tuning quality, while minimizing the need for frequent user annotations.