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A Survey of Personalized Large Language Models: Progress and Future Directions

17 February 2025
Jiahong Liu
Zexuan Qiu
Zhongyang Li
Quanyu Dai
Jieming Zhu
Minda Hu
Menglin Yang
Irwin King
    LM&MA
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Abstract

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 thethis https URL.

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@article{liu2025_2502.11528,
  title={ A Survey of Personalized Large Language Models: Progress and Future Directions },
  author={ Jiahong Liu and Zexuan Qiu and Zhongyang Li and Quanyu Dai and Jieming Zhu and Minda Hu and Menglin Yang and Irwin King },
  journal={arXiv preprint arXiv:2502.11528},
  year={ 2025 }
}
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