AI生成文本检测器的可靠性如何？一种使用规避型软提示的评估框架
How Reliable Are AI-Generated-Text Detectors? An Assessment Framework Using Evasive Soft Prompts
AI总结：
本研究针对AI生成文本检测器的可靠性问题，提出基于通用规避型软提示的评估方法，通过提示调优与软提示迁移实现对主流检测器的规避，并用多模型多写作任务实验验证其效果。
摘要：
近年来，AI生成文本快速激增，这主要是由强大的预训练语言模型（PLMs）的发布推动的。为解决AI生成文本相关的滥用问题，研究人员已开发出多种高性能检测器，包括OpenAI检测器和斯坦福大学的DetectGPT。在本研究中，我们探究这些检测器的可靠性究竟如何。我们通过设计一种新颖的方法来解答该问题，该方法可提示任意预训练语言模型（PLM）生成能规避这些高性能检测器的文本。所提出的方法构建了一种通用规避提示——这是一种新型软提示，可引导预训练语言模型生成能误导检测器的“类人”文本。这种新型通用规避提示通过两个步骤实现：首先，我们通过提示调优创建针对特定预训练语言模型的规避型软提示；随后，我们利用软提示的可迁移性，将已学习的规避型软提示从一个预训练语言模型迁移到另一个。我们在多种写作任务中使用多个预训练语言模型，开展了大量实验，以评估规避型软提示在规避最先进检测器方面的效果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
