  Large Language Models (LLMs) are capable of generating text that is similar
to or surpasses human quality. However, it is unclear whether LLMs tend to
exhibit distinctive linguistic styles akin to how human authors do. Through a
comprehensive linguistic analysis, we compare the vocabulary, Part-Of-Speech
(POS) distribution, dependency distribution, and sentiment of texts generated
by three of the most popular LLMS today (GPT-3.5, GPT-4, and Bard) to diverse
inputs. The results point to significant linguistic variations which, in turn,
enable us to attribute a given text to its LLM origin with a favorable 88\%
accuracy using a simple off-the-shelf classification model. Theoretical and
practical implications of this intriguing finding are discussed.
