Towards Personalized Intelligence at Scale
This addresses the challenge of enabling personalized intelligence at scale for applications requiring customized AI experiences, representing an incremental improvement in efficiency.
The paper tackles the computational scalability problem of personalized AI by introducing a Personalization Head (PH) attached to frozen pre-trained language models, which reduces model size and training costs compared to traditional fine-tuning while outperforming zeroshot baselines in F1 score.
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. Existing personalization approaches involve fine-tuning 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 industry-focused datasets and show that the PHs outperform zeroshot baseline in F1 score and are significantly more scalable than traditional fine-tuning approaches. We identify key factors required for effective PH design and training.