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 this https URL

大语言模型与机器生成文本（MGT）在新闻和教育等领域的广泛应用中出现了潜在的质量和信息完整性问题。本研究提出了混合文本形式 ——mixcase，并构建了 MixSet 数据集，旨在专门研究这些混合修改场景，并通过实验证明现有的 MGT 检测器在识别 mixcase 时存在困难，尤其是在处理微妙的修改和样式适应性方面。研究结果强调了为 mixcase 定制更细粒度的检测器的紧迫性，并为未来研究提供了有价值的见解。