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        <title>Almost AI, Almost Human: The Challenge of Detecting AI-Polished Writing</title>
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        <namePart type="given">Shoumik</namePart>
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            <namePart type="given">Wanxiang</namePart>
            <namePart type="family">Che</namePart>
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            <namePart type="given">Joyce</namePart>
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        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Shutova</namePart>
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    <abstract>The growing use of large language models (LLMs) for text generation has led to widespread concerns about AI-generated content detection. However, an overlooked challenge is AI-polished text, where human-written content undergoes subtle refinements using AI tools. This raises a critical question: should minimally polished text be classified as AI-generated? Such classification can lead to false plagiarism accusations and misleading claims about AI prevalence in online content. In this study, we systematically evaluate *twelve* state-of-the-art AI-text detectors using our **AI-Polished-Text Evaluation (APT-Eval)** dataset, which contains 15K samples refined at varying AI-involvement levels. Our findings reveal that detectors frequently flag even minimally polished text as AI-generated, struggle to differentiate between degrees of AI involvement, and exhibit biases against older and smaller models. These limitations highlight the urgent need for more nuanced detection methodologies.</abstract>
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    <identifier type="doi">10.18653/v1/2025.findings-acl.1303</identifier>
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%0 Conference Proceedings
%T Almost AI, Almost Human: The Challenge of Detecting AI-Polished Writing
%A Saha, Shoumik
%A Feizi, Soheil
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F saha-feizi-2025-almost
%X The growing use of large language models (LLMs) for text generation has led to widespread concerns about AI-generated content detection. However, an overlooked challenge is AI-polished text, where human-written content undergoes subtle refinements using AI tools. This raises a critical question: should minimally polished text be classified as AI-generated? Such classification can lead to false plagiarism accusations and misleading claims about AI prevalence in online content. In this study, we systematically evaluate *twelve* state-of-the-art AI-text detectors using our **AI-Polished-Text Evaluation (APT-Eval)** dataset, which contains 15K samples refined at varying AI-involvement levels. Our findings reveal that detectors frequently flag even minimally polished text as AI-generated, struggle to differentiate between degrees of AI involvement, and exhibit biases against older and smaller models. These limitations highlight the urgent need for more nuanced detection methodologies.
%R 10.18653/v1/2025.findings-acl.1303
%U https://aclanthology.org/2025.findings-acl.1303/
%U https://doi.org/10.18653/v1/2025.findings-acl.1303
%P 25414-25431

Markdown (Informal)

[Almost AI, Almost Human: The Challenge of Detecting AI-Polished Writing](https://aclanthology.org/2025.findings-acl.1303/) (Saha & Feizi, Findings 2025)

• Almost AI, Almost Human: The Challenge of Detecting AI-Polished Writing (Saha & Feizi, Findings 2025)

ACL

• Shoumik Saha and Soheil Feizi. 2025. Almost AI, Almost Human: The Challenge of Detecting AI-Polished Writing. In Findings of the Association for Computational Linguistics: ACL 2025, pages 25414–25431, Vienna, Austria. Association for Computational Linguistics.