This paper is a survey of personalized large language models (LLMs), outlining different ways to adapt these models for user-specific needs. It analyzes how to personalize LLMs based on various user-specific data such as static attributes, interaction history, and pair-wise human preferences. The authors propose taxonomies for personalization granularity (user-level, persona-level, and global preference), techniques (RAG, prompting, representation learning, and RLHF), evaluation metrics (intrinsic and extrinsic), and datasets (with and without ground-truth text). The paper concludes by highlighting key challenges for the future of personalized LLMs, including the cold-start problem, stereotype and bias issues, privacy concerns, and the complexities of multimodality.