We find that large language models (LLMs) are more likely to modify human-written text than AI-generated text when tasked with rewriting. This tendency arises because LLMs often perceive AI-generated text as high-quality, leading to fewer modifications. We introduce a method to detect AI-generated content by prompting LLMs to rewrite text and calculating the editing distance of the output. We dubbed our geneRative AI Detection viA Rewriting method Raidar. Raidar significantly improves the F1 detection scores of existing AI content detection models -- both academic and commercial -- across various domains, including News, creative writing, student essays, code, Yelp reviews, and arXiv papers, with gains of up to 29 points. Operating solely on word symbols without high-dimensional features, our method is compatible with black box LLMs, and is inherently robust on new content. Our results illustrate the unique imprint of machine-generated text through the lens of the machines themselves.

翻译：摘要：我们发现，当要求大型语言模型（LLMs）执行改写任务时，它们更倾向于修改人类撰写的文本，而非AI生成的文本。这种倾向源于LLMs通常将AI生成的文本视为高质量内容，从而减少修改量。我们提出一种检测AI生成内容的方法：提示LLMs改写文本，并计算输出的编辑距离。我们将该方法命名为“基于改写的生成式AI检测”（Raidar）。在新闻、创意写作、学生论文、代码、Yelp评论及arXiv论文等多个领域，Raidar显著提升了现有AI内容检测模型（包括学术与商业模型）的F1检测分数，最高提升达29个百分点。该方法仅依赖于文字符号运作，无需高维特征，可兼容黑盒LLMs，并天然具备对新内容的鲁棒性。我们的结果通过机器自身的视角揭示了机器生成文本的独特印记。