  The rapid advancement of AI technology has made text generation tools like
GPT-3 and ChatGPT increasingly accessible, scalable, and effective. This can
pose serious threat to the credibility of various forms of media if these
technologies are used for plagiarism, including scientific literature and news
sources. Despite the development of automated methods for paraphrase
identification, detecting this type of plagiarism remains a challenge due to
the disparate nature of the datasets on which these methods are trained. In
this study, we review traditional and current approaches to paraphrase
identification and propose a refined typology of paraphrases. We also
investigate how this typology is represented in popular datasets and how
under-representation of certain types of paraphrases impacts detection
capabilities. Finally, we outline new directions for future research and
datasets in the pursuit of more effective paraphrase detection using AI.
