Soft Prompt Projection to Personalize Across Model Scales

As large language models (LLMs) are deployed to real world use cases, it is important that LLMs accurately adapt to individual end users to better act as personal assistants or writing aids. While small LLMs can fit on user devices and train on user data without compromising privacy, devices may need to rely on large server-side LLMs to solve complex tasks, and such models cannot be trained on user data without compromising user privacy. In this work, we present an approach to personalize large server-side LLMs without needing to expose user data to the large model. We first learn personalization parameters for a small LLM on-device and then learn a projection network that maps these parameters to a large model. We show that our method enables large LLMs to personalize to entirely unseen users across tasks including text generation and rating prediction, achieving over 60% F1 on a 5-class rating prediction task where prompting baselines fail to even follow instructions. Our method combines the personalization of a small on-device LLM with the generation capabilities of a large server-side LLM, leading to superior win-rates over a generic large LLM and a personalized small LLM.