LLMs + Persona-Plug = Personalized LLMs

Understand

Personalization plays a critical role in numerous language tasks and applications, since users with the same requirements may prefer diverse outputs based on their individual interests.

• This has led to the development of various personalized approaches aimed at adapting large language models (LLMs) to generate customized outputs aligned with user preferences.
• Some of them involve fine-tuning a unique personalized LLM for each user, which is too expensive for widespread application.
• Alternative approaches introduce personalization information in a plug-and-play manner by retrieving the user's relevant historical texts as demonstrations.