With the remarkable development and widespread applications of large language models (LLMs), the use of machine-generated text (MGT) is becoming increasingly common. This trend brings potential risks, particularly to the quality and completeness of information in fields such as news and education. Current research predominantly addresses the detection of pure MGT without adequately addressing mixed scenarios including AI-revised Human-Written Text (HWT) or human-revised MGT. To confront this challenge, we introduce mixcase, a novel concept representing a hybrid text form involving both machine-generated and human-generated content. We collected mixcase instances generated from multiple daily text-editing scenarios and composed MixSet, the first dataset dedicated to studying these mixed modification scenarios. We conduct experiments to evaluate the efficacy of popular MGT detectors, assessing their effectiveness, robustness, and generalization performance. Our findings reveal that existing detectors struggle to identify mixcase as a separate class or MGT, particularly in dealing with subtle modifications and style adaptability. This research underscores the urgent need for more fine-grain detectors tailored for mixcase, offering valuable insights for future research. Code and Models are available at https://github.com/Dongping-Chen/MixSet.

翻译：随着大型语言模型（LLMs）的显著发展与广泛应用，机器生成文本（MGT）的使用日益普遍。这一趋势带来了潜在风险，尤其对新闻、教育等领域信息的质量与完整性构成挑战。现有研究主要聚焦于纯机器生成文本的检测，未能充分应对包含AI修改的人类撰写文本（HWT）或人类修改的机器生成文本等混合场景。为应对这一挑战，我们提出"混合文本"这一新概念，特指融合机器生成与人类生成内容的混合文本形式。我们收集了多种日常文本编辑场景中产生的混合文本实例，构建了首个专门研究此类混合修改场景的数据集MixSet。通过实验评估主流MGT检测器的有效性、鲁棒性与泛化性能，结果表明现有检测器难以将混合文本识别为独立类别或MGT，尤其在处理细微修改与风格适应性方面存在不足。本研究揭示了针对混合文本开发更精细化检测器的迫切需求，为未来研究提供了重要启示。代码与模型开源地址：https://github.com/Dongping-Chen/MixSet