Distinguishing Human Generated Text From ChatGPT Generated Text Using Machine Learning
This addresses concerns about AI-generated text detection, but it is incremental as it applies existing methods to a new dataset.
The paper tackles the problem of distinguishing ChatGPT-generated text from human-written text by comparing 11 machine learning and deep learning algorithms, achieving an accuracy of 77% on a dataset of 10,000 texts.
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%.