title: Personalization of Large Language Models: A Survey
summary: 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.
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paper_title: Personalization of Large Language Models: A Survey
paper_summary: 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.
paper_ai_keywords: Large Language Models (LLMs) personalized LLMs personalized text generation recommendation systems taxonomy personalization of LLMs personalization techniques evaluation methods applications of personalized LLMs