  The recent emergence of Large Language Models (LLMs) has heralded a new era
of human-AI interaction. These sophisticated models, exemplified by Chat-GPT
and its successors, have exhibited remarkable capabilities in language
understanding. However, as these LLMs have undergone exponential growth, a
crucial dimension that remains understudied is the personalization of these
models. Large foundation models such as GPT-3 etc. focus on creating a
universal model that serves a broad range of tasks and users. This approach
emphasizes the model's generalization capabilities, treating users as a
collective rather than as distinct individuals. While practical for many common
applications, this one-size-fits-all approach often fails to address the rich
tapestry of human diversity and individual needs. To explore this issue we
introduce the PEFT-U Benchmark: a new dataset for building and evaluating NLP
models for user personalization. \datasetname{} consists of a series of
user-centered tasks containing diverse and individualized expressions where the
preferences of users can potentially differ for the same input. Using PEFT-U,
we explore the challenge of efficiently personalizing LLMs to accommodate
user-specific preferences in the context of diverse user-centered tasks.
