Abstract
Detecting AI-involved text is essential for combating misinformation, plagiarism, and academic misconduct. However, AI text generation includes diverse collaborative processes (AI-written text edited by humans, human-written text edited by AI, and AI-generated text refined by other AI), where various or even new LLMs could be involved. Texts generated through these varied processes exhibit complex characteristics, presenting significant challenges for detection. Current methods model these processes rather crudely, primarily employing binary classification (purely human vs. AI-involved) or multi-classification (treating human-AI collaboration as a new class). We observe that representations of texts generated through different processes exhibit inherent clustering relationships. Therefore, we propose DETree, a novel approach that models the relationships among different processes as a Hierarchical Affinity Tree structure, and introduces a specialized loss function that aligns text representations with this tree. To facilitate this learning, we developed RealBench, a comprehensive benchmark dataset that automatically incorporates a wide spectrum of hybrid texts produced through various human-AI collaboration processes. Our method improves performance in hybrid text detection tasks and significantly enhances robustness and generalization in out-of-distribution scenarios, particularly in few-shot learning conditions, further demonstrating the promise of training-based approaches in OOD settings. Our code and dataset are available at this https URL.
Abstract (translated)
检测包含AI参与的文本对于打击错误信息、抄袭和学术不端行为至关重要。然而，AI生成的文本包括各种协作过程（如由人类编辑过的AI写作、经过AI编辑的人类写作以及被其他AI进一步精炼的AI生成文本），这些过程中可能涉及不同的甚至是新的大型语言模型(LLMs)。通过这些多样化的过程产生的文本具有复杂的特性，为检测带来了重大挑战。当前的方法较为粗糙地建模这些过程，主要采用二元分类（纯粹由人类编写 vs. 包含AI参与）或多类别分类（将人机协作视为一个新类别）。我们观察到，不同过程中生成的文本在表示上存在固有的聚类关系。因此，我们提出了DETree这一创新方法，该方法通过层次亲和树结构来建模各种过程之间的关系，并引入了一种特殊的损失函数以使文本表示与这种树相一致。为了支持这种方法的学习，我们开发了RealBench，这是一个全面的基准数据集，能够自动整合通过多种人机协作过程产生的广泛混合类型文本。我们的方法在混合文本检测任务中提高了性能，并显著增强了面对分布外（OOD）场景时的鲁棒性和泛化能力，特别是在少样本学习条件下。这进一步展示了基于训练的方法在OOD设置中的前景。我们的代码和数据集可以在以下链接获取：[此URL]。 注意：上述链接需要替换为实际可用的链接地址。
URL
https://arxiv.org/abs/2510.17489
