[2402.14873] Technical Report on the Pangram AI-Generated Text Classifier

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arXiv:2402.14873 (cs)

[Submitted on 21 Feb 2024 (v1), last revised 29 Jul 2024 (this version, v3)]

Title:Technical Report on the Pangram AI-Generated Text Classifier

Authors:Bradley Emi, Max Spero<br>View a PDF of the paper titled Technical Report on the Pangram AI-Generated Text Classifier, by Bradley Emi and Max Spero

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Abstract: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.

Subjects:

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

MSC classes:<br>68T50

ACM classes:<br>I.2.7

Cite as:<br>arXiv:2402.14873 [cs.CL]

(or<br>arXiv:2402.14873v3 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2402.14873

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arXiv-issued DOI via DataCite

Submission history<br>From: Maxwell Spero [view email]<br>[v1]<br>Wed, 21 Feb 2024 17:13:41 UTC (416 KB)

[v2]<br>Mon, 26 Feb 2024 05:28:41 UTC (418 KB)

[v3]<br>Mon, 29 Jul 2024 08:27:34 UTC (309 KB)

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