# Personalization of Large Language Models: A Survey

**Authors:** Zhehao Zhang, Ryan A. Rossi, Branislav Kveton, Yijia Shao, Diyi Yang, Hamed Zamani, Franck Dernoncourt, Joe Barrow, Tong Yu, Sungchul Kim, Ruiyi Zhang, Jiuxiang Gu, Tyler Derr, Hongjie Chen, Junda Wu, Xiang Chen, Zichao Wang, Subrata Mitra, Nedim Lipka, Nesreen Ahmed  
**arXiv:** [2411.00027](https://arxiv.org/abs/2411.00027)  
**Date:** 2024-10-29  
**Summary page:** https://www.summarizepaper.com/en/paper/2411.00027v1/

## Key points

- Thorough analysis of personalized Large Language Models (LLMs) and their application in various scenarios
- Proposal of three levels of personalization granularity
- Categorization and evaluation of different techniques for LLM personalization based on unique characteristics and strengths
- Presentation of a taxonomy of metrics and evaluation methods, emphasizing the importance of qualitative and quantitative measures in assessing personalized LLM performance
- Detailed categorization of datasets used for training and evaluation, highlighting the need for more diverse and representative data to advance research effectively

## Summary

This summary provides a thorough analysis of personalized Large Language Models (LLMs) and their application in various scenarios. It delves into the fundamental concepts and facets of personalization, outlining desiderata for their use and proposing three levels of personalization granularity. The different techniques for LLM personalization are categorized and evaluated based on their unique characteristics and strengths. A taxonomy of metrics and evaluation methods is also presented, highlighting the importance of both qualitative and quantitative measures in assessing personalized LLM performance. Additionally, a detailed categorization of datasets used for training and evaluation is provided, emphasizing the need for more diverse and representative data to advance research in this field effectively. Overall, this refined summary offers valuable insights into navigating the evolving landscape of personalized language models.