• Vasavi Sravanthi Balusa
• S. Hemanth Kumar
• Lavudya Tagore
• Gundoju Rahul

Artificial intelligence (AI) and natural language processing (NLP) technologies have impacted various domains, including text classification. One of the algorithms that has revolutionized text classification tasks is Bidirectional Encoder Representations from Transformers (BERT). This paper presents a comprehensive review of research related to the classification of human and AI- generated text using the BERT algorithm. We explore the theoretical foundations of BERT, its architecture, and its applications in text classification tasks. we analyze literature on the comparison of human- generated text and AI-generated text classification performance using BERT, highlighting challenges, advancements, and future directions in this field. This text generative model was a Large Language Model (LLM) so that it can produce text documents that seem to be written by natural intelligence. Although there are many 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 machine learning and deep learning algorithms in the classification process. We have collected a dataset consisting of texts written by humans and collected from news and social media.

BERT-BASED CLASSIFICATION OF HUMAN AND AI GENERATED TEXT

"BERT-BASED CLASSIFICATION OF HUMAN AND AI GENERATED TEXT", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.g367-g371, July-2024, Available :http://www.jetir.org/papers/JETIR2407642.pdf

"BERT-BASED CLASSIFICATION OF HUMAN AND AI GENERATED TEXT", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. ppg367-g371, July-2024, Available at : http://www.jetir.org/papers/JETIR2407642.pdf