  We find that large language models (LLMs) are more likely to modify
human-written text than AI-generated text when tasked with rewriting. This
tendency arises because LLMs often perceive AI-generated text as high-quality,
leading to fewer modifications. We introduce a method to detect AI-generated
content by prompting LLMs to rewrite text and calculating the editing distance
of the output. We dubbed our geneRative AI Detection viA Rewriting method
Raidar. Raidar significantly improves the F1 detection scores of existing AI
content detection models -- both academic and commercial -- across various
domains, including News, creative writing, student essays, code, Yelp reviews,
and arXiv papers, with gains of up to 29 points. Operating solely on word
symbols without high-dimensional features, our method is compatible with black
box LLMs, and is inherently robust on new content. Our results illustrate the
unique imprint of machine-generated text through the lens of the machines
themselves.
