  In the realm of text manipulation and linguistic transformation, the question
of authorship has been a subject of fascination and philosophical inquiry. Much
like the Ship of Theseus paradox, which ponders whether a ship remains the same
when each of its original planks is replaced, our research delves into an
intriguing question: Does a text retain its original authorship when it
undergoes numerous paraphrasing iterations? Specifically, since Large Language
Models (LLMs) have demonstrated remarkable proficiency in both the generation
of original content and the modification of human-authored texts, a pivotal
question emerges concerning the determination of authorship in instances where
LLMs or similar paraphrasing tools are employed to rephrase the text--i.e.,
whether authorship should be attributed to the original human author or the
AI-powered tool. Therefore, we embark on a philosophical voyage through the
seas of language and authorship to unravel this intricate puzzle. Using a
computational approach, we discover that the diminishing performance in text
classification models, with each successive paraphrasing iteration, is closely
associated with the extent of deviation from the original author's style, thus
provoking a reconsideration of the current notion of authorship.
