Towards Personalized Intelligence at Scale

Abstract

Personalized Intelligence (PI) is the problem of providing customized AI experiences tailored to each individual user. In many applications, PI is preferred or even required (Martinez et al., 2017;Rudovic et al., 2018). Existing personalization approaches involve finetuning pre-trained models to create new customized models. However, these approaches require a significant amount of computation to train, scaling with model size and the number of users, inhibiting PI to be realized widely. In this work, we introduce a novel model architecture and training/inference framework to enable Personalized Intelligence at scale. We achieve this by attaching a Personalization Head (PH) to pre-trained language models (LM). During training, the base LMs are frozen and only the parameters in PH are updated and are unique per user. This results in significantly smaller overall model sizes and training cost than traditional fine-tuning approaches when scaled across many users. We evaluate PHs on academia and industryfocused datasets and show that the PHs outperform zeroshot baseline in F1 score and are significantly more scalable than traditional finetuning approaches. We identify key factors required for effective PH design and training.