[2501.15654] People who frequently use ChatGPT for writing tasks are accurate and robust detectors of AI-generated text

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Computer Science > Computation and Language

arXiv:2501.15654 (cs)

[Submitted on 26 Jan 2025 (v1), last revised 19 May 2025 (this version, v2)]

Title:People who frequently use ChatGPT for writing tasks are accurate and robust detectors of AI-generated text

Authors:Jenna Russell, Marzena Karpinska, Mohit Iyyer<br>View a PDF of the paper titled People who frequently use ChatGPT for writing tasks are accurate and robust detectors of AI-generated text, by Jenna Russell and 2 other authors

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Abstract:In this paper, we study how well humans can detect text generated by commercial LLMs (GPT-4o, Claude, o1). We hire annotators to read 300 non-fiction English articles, label them as either human-written or AI-generated, and provide paragraph-length explanations for their decisions. Our experiments show that annotators who frequently use LLMs for writing tasks excel at detecting AI-generated text, even without any specialized training or feedback. In fact, the majority vote among five such "expert" annotators misclassifies only 1 of 300 articles, significantly outperforming most commercial and open-source detectors we evaluated even in the presence of evasion tactics like paraphrasing and humanization. Qualitative analysis of the experts' free-form explanations shows that while they rely heavily on specific lexical clues ('AI vocabulary'), they also pick up on more complex phenomena within the text (e.g., formality, originality, clarity) that are challenging to assess for automatic detectors. We release our annotated dataset and code to spur future research into both human and automated detection of AI-generated text.

Comments:<br>ACL 2025 33 pages

Subjects:

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

Cite as:<br>arXiv:2501.15654 [cs.CL]

(or<br>arXiv:2501.15654v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2501.15654

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arXiv-issued DOI via DataCite

Submission history<br>From: Jenna Russell [view email]<br>[v1]<br>Sun, 26 Jan 2025 19:31:34 UTC (8,174 KB)

[v2]<br>Mon, 19 May 2025 18:22:33 UTC (9,435 KB)

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