  As the text generation capabilities of large language models become
increasingly prominent, recent studies have focused on controlling particular
aspects of the generated text to make it more personalized. However, most
research on controllable text generation focuses on controlling the content or
modeling specific high-level/coarse-grained attributes that reflect authors'
writing styles, such as formality, domain, or sentiment. In this paper, we
focus on controlling fine-grained attributes spanning multiple linguistic
dimensions, such as lexical and syntactic attributes. We introduce a novel
benchmark to train generative models and evaluate their ability to generate
personalized text based on multiple fine-grained linguistic attributes. We
systematically investigate the performance of various large language models on
our benchmark and draw insights from the factors that impact their performance.
We make our code, data, and pretrained models publicly available.
