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. © 2024 Chinese Academy of Sciences. All rights reserved.