Enhancing Text Authenticity: A Novel Hybrid Approach for AI-Generated Text Detection

TL;DR

This paper introduces a hybrid detection method combining traditional TF-IDF features with advanced machine learning models, including deep learning, to improve the accuracy of distinguishing AI-generated text from human writing.

Contribution

It presents a novel hybrid approach that integrates traditional and deep learning techniques for more effective AI-generated text detection, outperforming existing methods.

Findings

The proposed method achieves higher accuracy than previous approaches.

Deep learning models significantly enhance detection performance.

The approach is effective across diverse datasets.

Abstract

The rapid advancement of Large Language Models (LLMs) has ushered in an era where AI-generated text is increasingly indistinguishable from human-generated content. Detecting AI-generated text has become imperative to combat misinformation, ensure content authenticity, and safeguard against malicious uses of AI. In this paper, we propose a novel hybrid approach that combines traditional TF-IDF techniques with advanced machine learning models, including Bayesian classifiers, Stochastic Gradient Descent (SGD), Categorical Gradient Boosting (CatBoost), and 12 instances of Deberta-v3-large models. Our approach aims to address the challenges associated with detecting AI-generated text by leveraging the strengths of both traditional feature extraction methods and state-of-the-art deep learning models. Through extensive experiments on a comprehensive dataset, we demonstrate the effectiveness of our proposed method in accurately distinguishing between human and AI-generated text. Our approach achieves superior performance compared to existing methods. This research contributes to the advancement of AI-generated text detection techniques and lays the foundation for developing robust solutions to mitigate the challenges posed by AI-generated content.