Research on Content Detection Algorithms and Bypass Mechanisms for Large Language Models
<p>Xueting Lin<sup>1</sup>, Yuming Tu<sup>2</sup>, Qingyi Lu<sup>3</sup>, Jinghan Cao<sup>4</sup>, Haowei Yang<sup>5</sup> </p> · Academic Journal of Computing & Information Science · 2025
The rapid advancement of technology is profoundly transforming the methods of information creation and dissemination, with the advent of large language models standing out as particularly significant. These models, with their formidable generative capabilities, have ushered in revolutionary applications in creative writing, technical generation, adaptive conversation, and other domains. However, the inevitable concern that accompanies this development is the challenge of detecting and regulating content generation. The misuse of large language models could precipitate an inundation of misinformation, while the inherent complexity and diversity of their generated content pose significant challenges to traditional detection methods. Consequently, the development of effective content detection algorithms has become an urgent priority. Meanwhile, the ongoing evolution of evasion mechanisms continually tests the limits of detection systems. The accurate distinction between computer-generated and human-authored content has emerged as a central focus of current research. The nuanced interplay between content detection and evasion mechanisms provides a crucial perspective for the study of information processing in the digital age.