A Survey of Personalized Large Language Models: Progress and Future Directions
It tackles the problem of improving user-specific personalization in LLMs for broader applications, but it is incremental as it surveys existing progress rather than introducing new methods.
This survey addresses the challenge of personalizing large language models (LLMs) to understand individual user needs like emotions and preferences, reviewing advancements in prompting, finetuning, and alignment techniques to enhance user satisfaction in applications such as conversational agents and recommendation systems.
Large Language Models (LLMs) excel in handling general knowledge tasks, yet they struggle with user-specific personalization, such as understanding individual emotions, writing styles, and preferences. Personalized Large Language Models (PLLMs) tackle these challenges by leveraging individual user data, such as user profiles, historical dialogues, content, and interactions, to deliver responses that are contextually relevant and tailored to each user's specific needs. This is a highly valuable research topic, as PLLMs can significantly enhance user satisfaction and have broad applications in conversational agents, recommendation systems, emotion recognition, medical assistants, and more. This survey reviews recent advancements in PLLMs from three technical perspectives: prompting for personalized context (input level), finetuning for personalized adapters (model level), and alignment for personalized preferences (objective level). To provide deeper insights, we also discuss current limitations and outline several promising directions for future research. Updated information about this survey can be found at the https://github.com/JiahongLiu21/Awesome-Personalized-Large-Language-Models.