  ChatGPT has become a global sensation. As ChatGPT and other Large Language
Models (LLMs) emerge, concerns of misusing them in various ways increase, such
as disseminating fake news, plagiarism, manipulating public opinion, cheating,
and fraud. Hence, distinguishing AI-generated from human-generated becomes
increasingly essential. Researchers have proposed various detection
methodologies, ranging from basic binary classifiers to more complex
deep-learning models. Some detection techniques rely on statistical
characteristics or syntactic patterns, while others incorporate semantic or
contextual information to improve accuracy. The primary objective of this study
is to provide a comprehensive and contemporary assessment of the most recent
techniques in ChatGPT detection. Additionally, we evaluated other AI-generated
text detection tools that do not specifically claim to detect ChatGPT-generated
content to assess their performance in detecting ChatGPT-generated content. For
our evaluation, we have curated a benchmark dataset consisting of prompts from
ChatGPT and humans, including diverse questions from medical, open Q&A, and
finance domains and user-generated responses from popular social networking
platforms. The dataset serves as a reference to assess the performance of
various techniques in detecting ChatGPT-generated content. Our evaluation
results demonstrate that none of the existing methods can effectively detect
ChatGPT-generated content.
