Detecting ChatGPT Generated Texts Based on Deep Pyramid Convolutional Neural Network
[Objective]This paper develops a method detecting ChatGPT(AI)generated Chinese texts to prevent the misuse of ChatGPT.[Methods]We constructed three Chinese datasets using the prompt-based approach.We then conducted model training and testing on these three datasets and identified an optimal AI-generated text detection method based on dimensions like model type,text type,and text length.[Results]Through various comparative approaches,the text classification method based on the Deep Pyramid Convolutional Neural Network(DPCNN)achieved an accuracy of 0.9655 on the test set,outperforming other methods.Furthermore,the DPCNN model demonstrated strong cross-category capability.The length of the texts affects the model's accuracy.[Limitations]The Chinese dataset generated by the prompt-based approach has limitations in category diversity,as only three types of datasets were constructed and used for model training.[Conclusions]This paper proposes a method for detecting AI-generated text in the Chinese context,where accuracy is influenced by text type and text length.
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