Enhancing Text Authenticity: A Novel Hybrid Approach for AI-Generated Text Detection
<|reference_start|>Enhancing Text Authenticity: A Novel Hybrid Approach for AI-Generated Text Detection: 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.<|reference_end|>
@article{zhang2024enhancing,
    title={Enhancing Text Authenticity: A Novel Hybrid Approach for AI-Generated
  Text Detection},
    author={Ye Zhang, Qian Leng, Mengran Zhu, Rui Ding, Yue Wu, Jintong Song, Yulu
  Gong},
    journal={arXiv preprint arXiv:2406.06558},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06558},
    primaryClass={cs.CL cs.AI}
}