Technical Report on the Pangram AI-Generated Text Classifier
<|reference_start|>Technical Report on the Pangram AI-Generated Text Classifier: 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.<|reference_end|>
@article{emi2024technical,
    title={Technical Report on the Pangram AI-Generated Text Classifier},
    author={Bradley Emi and Max Spero},
    journal={arXiv preprint arXiv:2402.14873},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.14873},
    primaryClass={cs.CL cs.AI}
}