Almost AI, Almost Human: The Challenge of Detecting AI-Polished Writing

• URL: http://arxiv.org/abs/2502.15666v1
• Date: Fri, 21 Feb 2025 18:45:37 GMT
• Title: Almost AI, Almost Human: The Challenge of Detecting AI-Polished Writing
• Authors: Shoumik Saha, Soheil Feizi,
• Abstract summary: Misclassification can lead to false plagiarism accusations and misleading claims about AI prevalence in online content.<n>We systematically evaluate eleven state-of-the-art AI-text detectors using our AI-Polished-Text Evaluation dataset.<n>Our findings reveal that detectors frequently misclassify even minimally polished text as AI-generated, struggle to differentiate between degrees of AI involvement, and exhibit biases against older and smaller models.
• Score: 55.2480439325792
• License: http://creativecommons.org/licenses/by/4.0/
• 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? Misclassification can lead to false plagiarism accusations and misleading claims about AI prevalence in online content. In this study, we systematically evaluate eleven state-of-the-art AI-text detectors using our AI-Polished-Text Evaluation (APT-Eval) dataset, which contains $11.7K$ samples refined at varying AI-involvement levels. Our findings reveal that detectors frequently misclassify 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.

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