Large Language Model (LLM) AI Text Generation Detection based on Transformer Deep Learning Algorithm


  • Yuhong Mo College of Engineering, Carnegie Mellon University, PA, Pittsburgh, 15213, USA
  • Hao Qin Independent, CHINA
  • Yushan Dong University of Maryland, MD, USA
  • Ziyi Zhu New York University, USA
  • Zhenglin Li Texas A&M University, USA



SVM, Stock Price Prediction, Time Series


In this paper, a tool for detecting LLM AI text generation is developed based on the Transformer model, aiming to improve the accuracy of AI text generation detection and provide reference for subsequent research. Firstly the text is Unicode normalised, converted to lowercase form, characters other than non-alphabetic characters and punctuation marks are removed by regular expressions, spaces are added around punctuation marks, first and last spaces are removed, consecutive ellipses are replaced with single spaces and the text is connected using the specified delimiter. Next remove non-alphabetic characters and extra whitespace characters, replace multiple consecutive whitespace characters with a single space and again convert to lowercase form. The deep learning model combines layers such as LSTM, Transformer and CNN for text classification or sequence labelling tasks. The training and validation sets show that the model loss decreases from 0.127 to 0.005 and accuracy increases from 94.96 to 99.8, indicating that the model has good detection and classification ability for AI generated text. The test set confusion matrix and accuracy show that the model has 99% prediction accuracy for AI-generated text, with a precision of 0.99, a recall of 1, and an f1 score of 0.99, achieving a very high classification accuracy. Looking forward, it has the prospect of wide application in the field of AI text detection.


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How to Cite

Mo, Y. ., Qin, H., Dong, Y., Zhu, Z., & Li, Z. (2024). Large Language Model (LLM) AI Text Generation Detection based on Transformer Deep Learning Algorithm. International Journal of Engineering and Management Research, 14(2), 154–159.