Enhancing Text Authenticity: A Novel Hybrid Approach for AI-Generated Text Detection
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911912337342464 |
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| author | Zhang, Ye Leng, Qian Zhu, Mengran Ding, Rui Wu, Yue Song, Jintong Gong, Yulu |
| author_facet | Zhang, Ye Leng, Qian Zhu, Mengran Ding, Rui Wu, Yue Song, Jintong Gong, Yulu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_06558 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Text Authenticity: A Novel Hybrid Approach for AI-Generated Text Detection Zhang, Ye Leng, Qian Zhu, Mengran Ding, Rui Wu, Yue Song, Jintong Gong, Yulu Computation and Language Artificial Intelligence 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. |
| title | Enhancing Text Authenticity: A Novel Hybrid Approach for AI-Generated Text Detection |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2406.06558 |