Teach LLMs to Personalize

Attribute-free personalization: Generates personalized text without predefined attributes like age, profession, or preferences - instead inferring style from user history.

Multitask learning: Frames personalization as a multitask problem where tasks correspond to different personalization axes, sharing representation across them.

Generalizable style: Demonstrates that models can adapt to new users with minimal examples when trained with this multitask approach.

Production relevance: Directly applicable to personalized-assistant and content-generation products where explicit user-profile attributes are impractical or privacy-sensitive.