Personalization of Large Language Models: A Survey
Authors: name: Zhehao Zhang, name: Ryan A. Rossi, name: Branislav Kveton, name: Yijia Shao, name: Diyi Yang, name: Hamed Zamani, name: Franck Dernoncourt, name: Joe Barrow, name: Tong Yu, name: Sungchul Kim, name: Ruiyi Zhang, name: Jiuxiang Gu, name: Tyler Derr, name: Hongjie Chen, name: Junda Wu, name: Xiang Chen, name: Zichao Wang, name: Subrata Mitra, name: Nedim Lipka, name: Nesreen Ahmed, name: Yu Wang

Personalization of Large Language Models (LLMs) has recently become
increasingly important with a wide range of applications. Despite the
importance and recent progress, most existing works on personalized LLMs have
focused either entirely on (a) personalized text generation or (b) leveraging
LLMs for personalization-related downstream applications, such as
recommendation systems. In this work, we bridge the gap between these two
separate main directions for the first time by introducing a taxonomy for
personalized LLM usage and summarizing the key differences and challenges. We
provide a formalization of the foundations of personalized LLMs that
consolidates and expands notions of personalization of LLMs, defining and
discussing novel facets of personalization, usage, and desiderata of
personalized LLMs. We then unify the literature across these diverse fields and
usage scenarios by proposing systematic taxonomies for the granularity of
personalization, personalization techniques, datasets, evaluation methods, and
applications of personalized LLMs. Finally, we highlight challenges and
important open problems that remain to be addressed. By unifying and surveying
recent research using the proposed taxonomies, we aim to provide a clear guide
to the existing literature and different facets of personalization in LLMs,
empowering both researchers and practitioners.