Rapidly increasing quality of AI-generated content makes it difficult to distinguish between human and AI-generated texts, which may lead to undesirable consequences for society. Therefore, it becomes increasingly important to study the properties of human texts that are invariant over text domains and various proficiency of human writers, can be easily calculated for any language, and can robustly separate natural and AI-generated texts regardless of the generation model and sampling method. In this work, we propose such an invariant of human texts, namely the intrinsic dimensionality of the manifold underlying the set of embeddings of a given text sample. We show that the average intrinsic dimensionality of fluent texts in natural language is hovering around the value $9$ for several alphabet-based languages and around $7$ for Chinese, while the average intrinsic dimensionality of AI-generated texts for each language is $\approx 1.5$ lower, with a clear statistical separation between human-generated and AI-generated distributions. This property allows us to build a score-based artificial text detector. The proposed detector's accuracy is stable over text domains, generator models, and human writer proficiency levels, outperforming SOTA detectors in model-agnostic and cross-domain scenarios by a significant margin.

翻译：快速提升的AI生成内容质量使得区分人类与AI生成文本变得困难，这可能给社会带来不良后果。因此，研究人类文本中具有跨文本领域、不同人类写作者能力水平不变性，且易于在任何语言中计算，并能鲁棒区分自然文本与AI生成文本（无论生成模型与采样方法为何）的特性日益重要。本文提出一种人类文本的此类不变量，即给定文本样本嵌入集所隐含流形的本征维度。研究表明，自然语言中流畅文本的平均本征维度在几种基于字母的语言中稳定在约9，中文约7，而各语言AI生成文本的平均本征维度低约1.5，且人类生成与AI生成文本的分布存在显著的统计分离。该特性使我们能够构建基于分数的AI文本检测器。所提检测器在文本领域、生成模型及人类写作者能力水平上保持稳定的准确率，在模型无关和跨领域场景下显著优于现有最优检测器。