AI 中文总结
MixDetect提出词级框架，通过预测每个词的编辑状态和程度，实现AI编辑的定位与量化，实验验证其准确性和有效性。
AI 中文摘要
大型语言模型越来越多地被用于编辑人类撰写的文本，而非从头生成整篇文本。传统的AI文本检测器主要区分人类撰写与完全由AI生成的文本，而近期针对AI编辑文本的方法通常仅提供文本级标签或编辑程度评分。我们提出MixDetect，一个用于定位和量化AI编辑的词级框架。MixDetect分别预测每个词是否被编辑，以及在编辑条件下编辑的程度，从而能够分别估计编辑范围和编辑强度。在训练期间，源文本与编辑后文本的配对被对齐以构建词级监督，而推理时仅需输入文本。实验表明，MixDetect能够准确定位AI编辑的词，反映编辑强度的差异，并揭示不同编辑程度和操作下的范围-强度模式。在额外AI编辑下，整体AI编辑幅度增加；当AI生成的文本被人类编辑时，整体AI编辑幅度减少；而在普通人类对人类的编辑下，整体AI编辑幅度几乎保持不变。聚合的文本级预测在二元和三元AI文本分类上也表现良好，并在领域和生成器迁移下保持有效。这些结果表明，AI编辑可以超越单一的作者身份标签或编辑程度评分进行分析，通过识别AI编辑发生的位置以及编辑的程度来实现。
英文摘要
Large language models are increasingly used to edit human-written text rather than generate entire texts from scratch. Conventional AI-text detectors mainly distinguish human-written from fully AI-generated text, while recent methods for AI-edited text typically provide only a text-level label or editing-degree score. We introduce MixDetect, a word-level framework for localizing and quantifying AI editing. MixDetect separately predicts whether each word has been edited and, conditional on editing, how substantial the edit is, allowing editing scope and editing intensity to be estimated separately. During training, source--edited pairs are aligned to construct word-level supervision, while inference requires only the input text. Experiments show that MixDetect accurately localizes AI-edited words, reflects differences in editing intensity, and reveals different scope--intensity patterns across editing degrees and operations. The overall AI editing magnitude increases under additional AI editing, decreases when AI-generated text is edited by humans, and remains nearly unchanged under ordinary human-to-human editing. The aggregated text-level predictions also perform well on binary and ternary AI-text classification and remain effective under domain and generator shifts. These results show that AI editing can be analyzed beyond a single authorship label or editing-degree score by identifying both where AI editing occurs and how substantial the edits are.
Comments18 pages
