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.

本文提出了一种新颖的混合方法，将传统的TF-IDF技术与先进的机器学习模型相结合，包括贝叶斯分类器、随机梯度下降（SGD）、分类梯度提升（CatBoost）和12个Deberta-v3-large模型的实例。通过在全面的数据集上进行广泛的实验，我们证明了我们提出的方法在准确区分人工生成和AI生成文本方面的有效性。与现有方法相比，我们的方法取得了更好的性能。这项研究为AI生成文本检测技术的进展做出了贡献，并为应对AI生成内容带来的挑战开发出稳健的解决方案奠定了基础。

增强文本真实性：一种新颖的混合方法用于AI生成文本检测