AI-generated text detection has attracted increasing attention as powerful language models approach human-level generation. Limited work is devoted to detecting (partially) AI-paraphrased texts. However, AI paraphrasing is commonly employed in various application scenarios for text refinement and diversity. To this end, we propose a novel detection framework, paraphrased text span detection (PTD), aiming to identify paraphrased text spans within a text. Different from text-level detection, PTD takes in the full text and assigns each of the sentences with a score indicating the paraphrasing degree. We construct a dedicated dataset, PASTED, for paraphrased text span detection. Both in-distribution and out-of-distribution results demonstrate the effectiveness of PTD models in identifying AI-paraphrased text spans. Statistical and model analysis explains the crucial role of the surrounding context of the paraphrased text spans. Extensive experiments show that PTD models can generalize to versatile paraphrasing prompts and multiple paraphrased text spans. We release our resources at https://github.com/Linzwcs/PASTED.

翻译：AI生成文本的检测随着语言模型达到接近人类水平的生成能力而日益受到关注。然而，针对（部分）AI改写文本的检测研究仍十分有限。但在实际应用中，AI改写常被用于文本润色和多样性增强的场景。为此，我们提出一种新型检测框架——改写文本片段检测（PTD），旨在识别文本中被改写的文本片段。与文本级检测不同，PTD输入完整文本，并为每个句子分配一个表示改写程度的分数。我们构建了一个专用数据集PASTED，用于改写文本片段检测。分布内和分布外实验结果均表明，PTD模型在识别AI改写文本片段方面具有有效性。统计分析和模型分析解释了改写文本片段周围上下文的关键作用。大量实验表明，PTD模型能够泛化至多种改写提示和多段改写文本。我们在https://github.com/Linzwcs/PASTED公开了相关资源。