@inproceedings{saha-feizi-2025-almost,
    title = "Almost {AI}, Almost Human: The Challenge of Detecting {AI}-Polished Writing",
    author = "Saha, Shoumik  and
      Feizi, Soheil",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.findings-acl.1303/",
    pages = "25414--25431",
    ISBN = "979-8-89176-256-5",
    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."
}