How Reliable Are AI-Generated-Text Detectors? An Assessment Framework
  Using Evasive Soft Prompts
Tharindu Kumarage Paras Sheth Raha Moraffah Joshua Garland Huan Liu
In recent years, there has been a rapid proliferation of AI-generated text,
primarily driven by the release of powerful pre-trained language models (PLMs).
To address the issue of misuse associated with AI-generated text, various
high-performing detectors have been developed, including the OpenAI detector
and the Stanford DetectGPT. In our study, we ask how reliable these detectors
are. We answer the question by designing a novel approach that can prompt any
PLM to generate text that evades these high-performing detectors. The proposed
approach suggests a universal evasive prompt, a novel type of soft prompt,
which guides PLMs in producing "human-like" text that can mislead the
detectors. The novel universal evasive prompt is achieved in two steps: First,
we create an evasive soft prompt tailored to a specific PLM through prompt
tuning; and then, we leverage the transferability of soft prompts to transfer
the learned evasive soft prompt from one PLM to another. Employing multiple
PLMs in various writing tasks, we conduct extensive experiments to evaluate the
efficacy of the evasive soft prompts in their evasion of state-of-the-art
detectors.