区分文本是人工写、机器生成、润色还是翻译，提升检测精度。
Real, Fake, or Manipulated? Detecting Machine-Influenced Text
- 分层设计，用专用模型处理不同长度文本，提升鲁棒性。
- 在五种大模型六类场景下，平均比现有方法高2.5-3 mAP。
- 特别擅长区分易混淆类型，如不同语言的机器翻译文本。
大型语言模型（LLMs）可用于撰写或修改文档，带来意图识别挑战。例如，良性用途包括改善人工文本语法或翻译成其他语言；但完全由模型生成的文本更可能被用于传播虚假信息（如恶意行为者利用或幻觉）。以往机器生成文本检测研究多仅区分人类与机器写作，忽略细粒度使用场景。本文提出一种分层、长度鲁棒的机器影响文本检测器（HERO），可区分四类文本：人工撰写、机器生成、机器润色和机器翻译。HERO通过融合经子类别引导训练的长度专用模型预测结果实现分类。针对易混淆类别（如不同源语言），子类别引导模块促进细粒度分离，提升性能。在五种大模型、六类领域的广泛实验表明，HERO平均优于当前最优方法2.5-3 mAP。
原文摘要 · Abstract (English)
Large Language Model (LLMs) can be used to write or modify documents, presenting a challenge for understanding the intent behind their use. For example, benign uses may involve using LLM on a human-written document to improve its grammar or to translate it into another language. However, a document entirely produced by a LLM may be more likely to be used to spread misinformation than simple translation (\eg, from use by malicious actors or simply by hallucinating). Prior works in Machine Generated Text (MGT) detection mostly focus on simply identifying whether a document was human or machine written, ignoring these fine-grained uses. In this paper, we introduce a HiErarchical, length-RObust machine-influenced text detector (HERO), which learns to separate text samples of varying lengths from four primary types: human-written, machine-generated, machine-polished, and machine-translated. HERO accomplishes this by combining predictions from length-specialist models that have been trained with Subcategory Guidance. Specifically, for categories that are easily confused (\eg, different source languages), our Subcategory Guidance module encourages separation of the fine-grained categories, boosting performance. Extensive experiments across five LLMs and six domains demonstrate the benefits of our HERO, outperforming the state-of-the-art by 2.5-3 mAP on average.
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