[2606.04906] 'Your AI Text is not Mine': Redefining and Evaluating AI-generated Text Detection under Realistic Assumptions

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Computer Science > Computation and Language

arXiv:2606.04906 (cs)

[Submitted on 3 Jun 2026]

Title:'Your AI Text is not Mine': Redefining and Evaluating AI-generated Text Detection under Realistic Assumptions

Authors:Nils Dycke, Marina Sakharova, Nico Daheim, Iryna Gurevych<br>View a PDF of the paper titled 'Your AI Text is not Mine': Redefining and Evaluating AI-generated Text Detection under Realistic Assumptions, by Nils Dycke and 3 other authors

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Abstract:Although it is generally agreed that AI-generated text poses a broad societal risk, there is no common understanding in the AI-generated text detection literature on what constitutes harmful use. Rather, existing datasets and approaches often define their own criteria and make their own assumptions, sometimes implicitly, and often only loosely related to real-world needs and applications. To address this gap, we here systematically define various notions of AI-generated text and their characteristics. To study these, we collect AITDNA - a new benchmark of human-machine co-constructed texts that is annotated with detailed genesis information, such as the entire edit and AI-interaction history. We benchmark various machine-generated text detectors and find that they often only perform well for specific notions but not as broad detectors. We release code and data publicly.

Subjects:

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

Cite as:<br>arXiv:2606.04906 [cs.CL]

(or<br>arXiv:2606.04906v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.04906

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Marina Sakharova [view email]<br>[v1]<br>Wed, 3 Jun 2026 14:05:14 UTC (663 KB)

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