  With the increasing use of Artificial Intelligence in Natural Language
Processing, concerns have been raised regarding the detection of AI-generated
text in various domains. This study aims to investigate this issue by proposing
a methodology to accurately distinguish AI-generated and human-written book
reviews. Our approach utilizes transfer learning, enabling the model to
identify generated text across different topics while improving its ability to
detect variations in writing style and vocabulary. To evaluate the
effectiveness of the proposed methodology, we developed a dataset consisting of
real book reviews and AI-generated reviews using the recently proposed Vicuna
open-source language model. The experimental results demonstrate that it is
feasible to detect the original source of text, achieving an accuracy rate of
96.86%. Our efforts are oriented toward the exploration of the capabilities and
limitations of Large Language Models in the context of text identification.
Expanding our knowledge in these aspects will be valuable for effectively
navigating similar models in the future and ensuring the integrity and
authenticity of human-generated content.
