  The introduction of ChatGPT has garnered widespread attention in both
academic and industrial communities. ChatGPT is able to respond effectively to
a wide range of human questions, providing fluent and comprehensive answers
that significantly surpass previous public chatbots in terms of security and
usefulness. On one hand, people are curious about how ChatGPT is able to
achieve such strength and how far it is from human experts. On the other hand,
people are starting to worry about the potential negative impacts that large
language models (LLMs) like ChatGPT could have on society, such as fake news,
plagiarism, and social security issues. In this work, we collected tens of
thousands of comparison responses from both human experts and ChatGPT, with
questions ranging from open-domain, financial, medical, legal, and
psychological areas. We call the collected dataset the Human ChatGPT Comparison
Corpus (HC3). Based on the HC3 dataset, we study the characteristics of
ChatGPT's responses, the differences and gaps from human experts, and future
directions for LLMs. We conducted comprehensive human evaluations and
linguistic analyses of ChatGPT-generated content compared with that of humans,
where many interesting results are revealed. After that, we conduct extensive
experiments on how to effectively detect whether a certain text is generated by
ChatGPT or humans. We build three different detection systems, explore several
key factors that influence their effectiveness, and evaluate them in different
scenarios. The dataset, code, and models are all publicly available at
https://github.com/Hello-SimpleAI/chatgpt-comparison-detection.
