We introduce Ghostbuster, a state-of-the-art system for detecting AI-generated text. Our method works by passing documents through a series of weaker language models and running a structured search over possible combinations of their features, then training a classifier on the selected features to determine if the target document was AI-generated. Crucially, Ghostbuster does not require access to token probabilities from the target model, making it useful for detecting text generated by black-box models or unknown model versions. In conjunction with our model, we release three new datasets of human and AI-generated text as detection benchmarks that cover multiple domains (student essays, creative fiction, and news) and task setups: document-level detection, author identification, and a challenge task of paragraph-level detection. Ghostbuster averages 99.1 F1 across all three datasets on document-level detection, outperforming previous approaches such as GPTZero and DetectGPT by up to 32.7 F1.

翻译：我们引入Ghostbuster，一种用于检测AI生成文本的最先进系统。该方法通过将文档输入一系列较弱的语言模型，对其特征的可能组合进行结构化搜索，随后基于所选特征训练分类器，以判断目标文档是否为AI生成。关键在于，Ghostbuster无需访问目标模型的token概率，因此可有效检测黑盒模型或未知版本模型生成的文本。配合本模型，我们发布了三个全新的人类与AI生成文本检测基准数据集，涵盖多领域（学生论文、创意小说及新闻）与多种任务设置：文档级检测、作者识别以及段落级检测挑战任务。在三个数据集的文档级检测中，Ghostbuster的F1值平均达99.1，较GPTZero与DetectGPT等先前方法最高提升32.7个F1点。