PERSOMA: PERsonalized SOft ProMpt Adapter Architecture for Personalized Language Prompting
Abstract
Understanding extensive historical user interactions is pivotal in
capturing users’ evolving preferences, enabling more precise and
personalized natural language systems. To tackle this challenge,
we introduce the PERSOMA: Personalized Soft Prompt Adapter
architecture. In contrast to previous work in personalized prompt-
ing using large language models, PERSOMA introduces a novel
approach to efficiently capture user history in free-form text by re-
sampling and compressing interactions as expressive soft prompt
embeddings. We validate our approach through an extensive evalua-
tion of various adapter architectures, first stage sampling strategies,
and other personalization methods. Our results demonstrate the
superior capability of PERSOMA in handling large complex histories
compared to previous embedding-based and text-prompt based methods.
capturing users’ evolving preferences, enabling more precise and
personalized natural language systems. To tackle this challenge,
we introduce the PERSOMA: Personalized Soft Prompt Adapter
architecture. In contrast to previous work in personalized prompt-
ing using large language models, PERSOMA introduces a novel
approach to efficiently capture user history in free-form text by re-
sampling and compressing interactions as expressive soft prompt
embeddings. We validate our approach through an extensive evalua-
tion of various adapter architectures, first stage sampling strategies,
and other personalization methods. Our results demonstrate the
superior capability of PERSOMA in handling large complex histories
compared to previous embedding-based and text-prompt based methods.
Research Areas
