用压缩率差异识别AI生成文本，速度快且无需GPU。
AIDetx: a compression-based method for identification of machine-learning generated text
- 基于有限上下文模型构建人类与AI文本的压缩模型
- 在两个数据集上F1分数分别超97%和99%
- 解释性强、计算开销低，适合资源受限场景
本文提出AIDetx，一种基于数据压缩的机器生成文本检测方法。传统深度学习分类器常面临计算成本高、可解释性差的问题。为此，我们设计了一种压缩分类框架，利用有限上下文模型（FCMs）为人类写作和AI生成文本构建不同的压缩模型，通过比较新文本在哪个模型中压缩率更高来实现分类。在两个基准数据集上的实验显示，AIDetx的F1分数分别超过97%和99%，表现出极高准确性。相比现有方法（如大语言模型），AIDetx具有更强的可解释性与更低的计算开销，显著降低训练时间和硬件需求（例如无需GPU）。完整代码已公开于https://github.com/AIDetx/AIDetx。
原文摘要 · Abstract (English)
This paper introduces AIDetx, a novel method for detecting machine-generated text using data compression techniques. Traditional approaches, such as deep learning classifiers, often suffer from high computational costs and limited interpretability. To address these limitations, we propose a compression-based classification framework that leverages finite-context models (FCMs). AIDetx constructs distinct compression models for human-written and AI-generated text, classifying new inputs based on which model achieves a higher compression ratio. We evaluated AIDetx on two benchmark datasets, achieving F1 scores exceeding 97% and 99%, respectively, highlighting its high accuracy. Compared to current methods, such as large language models (LLMs), AIDetx offers a more interpretable and computationally efficient solution, significantly reducing both training time and hardware requirements (e.g., no GPUs needed). The full implementation is publicly available at https://github.com/AIDetx/AIDetx.
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