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

**Authors:** Shoumik Saha, Soheil Feizi  
**arXiv:** [2502.15666](https://arxiv.org/abs/2502.15666)  
**Date:** 2025-02-21  
**Summary page:** https://www.summarizepaper.com/fr/paper/2502.15666v1/

## 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.