Distinguishing Human Generated Text From ChatGPT Generated Text Using
  Machine Learning
Niful Islam Debopom Sutradhar Humaira Noor Jarin Tasnim Raya Monowara Tabassum Maisha Dewan Md Farid
ChatGPT is a conversational artificial intelligence that is a member of the
generative pre-trained transformer of the large language model family. This
text generative model was fine-tuned by both supervised learning and
reinforcement learning so that it can produce text documents that seem to be
written by natural intelligence. Although there are numerous advantages of this
generative model, it comes with some reasonable concerns as well. This paper
presents a machine learning-based solution that can identify the ChatGPT
delivered text from the human written text along with the comparative analysis
of a total of 11 machine learning and deep learning algorithms in the
classification process. We have tested the proposed model on a Kaggle dataset
consisting of 10,000 texts out of which 5,204 texts were written by humans and
collected from news and social media. On the corpus generated by GPT-3.5, the
proposed algorithm presents an accuracy of 77%.