  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.
