Detecting AI Generated Text Based on NLP and Machine Learning Approaches

TL;DR

This paper develops and evaluates machine learning models, including BERT, to accurately distinguish AI-generated text from human-written text, highlighting societal implications and achieving high accuracy.

Contribution

Introduces an effective AI text detection model using NLP and machine learning, with BERT outperforming previous models in accuracy.

Findings

BERT achieves 93% accuracy in detecting AI-generated text

XGB classifier and SVM achieve 84% and 81% accuracy respectively

The proposed approach has positive societal and ethical implications

Abstract

Recent advances in natural language processing (NLP) may enable artificial intelligence (AI) models to generate writing that is identical to human written form in the future. This might have profound ethical, legal, and social repercussions. This study aims to address this problem by offering an accurate AI detector model that can differentiate between electronically produced text and human-written text. Our approach includes machine learning methods such as XGB Classifier, SVM, BERT architecture deep learning models. Furthermore, our results show that the BERT performs better than previous models in identifying information generated by AI from information provided by humans. Provide a comprehensive analysis of the current state of AI-generated text identification in our assessment of pertinent studies. Our testing yielded positive findings, showing that our strategy is successful, with the BERT emerging as the most probable answer. We analyze the research's societal implications, highlighting the possible advantages for various industries while addressing sustainability issues pertaining to morality and the environment. The XGB classifier and SVM give 0.84 and 0.81 accuracy in this article, respectively. The greatest accuracy in this research is provided by the BERT model, which provides 0.93% accuracy.