  We present Pangram Text, a transformer-based neural network trained to
distinguish text written by large language models from text written by humans.
Pangram Text outperforms zero-shot methods such as DetectGPT as well as leading
commercial AI detection tools with over 38 times lower error rates on a
comprehensive benchmark comprised of 10 text domains (student writing, creative
writing, scientific writing, books, encyclopedias, news, email, scientific
papers, short-form Q&A) and 8 open- and closed-source large language models. We
propose a training algorithm, hard negative mining with synthetic mirrors, that
enables our classifier to achieve orders of magnitude lower false positive
rates on high-data domains such as reviews. Finally, we show that Pangram Text
is not biased against nonnative English speakers and generalizes to domains and
models unseen during training.
