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With the remarkable development and widespread applications of large languagemodels (LLMs), the use of machine-generated text (MGT) is becoming increasinglycommon. This trend brings potential risks, particularly to the quality andcompleteness of information in fields such as news and education. Currentresearch predominantly addresses the detection of pure MGT without adequatelyaddressing mixed scenarios including AI-revised Human-Written Text (HWT) orhuman-revised MGT. To confront this challenge, we introduce mixcase, a novelconcept representing a hybrid text form involving both machine-generated andhuman-generated content. We collected mixcase instances generated from multipledaily text-editing scenarios and composed MixSet, the first dataset dedicatedto studying these mixed modification scenarios. We conduct experiments toevaluate the efficacy of popular MGT detectors, assessing their effectiveness,robustness, and generalization performance. Our findings reveal that existingdetectors struggle to identify mixcase as a separate class or MGT, particularlyin dealing with subtle modifications and style adaptability. This researchunderscores the urgent need for more fine-grain detectors tailored for mixcase,offering valuable insights for future research. Code and Models are availableat https://github.com/Dongping-Chen/MixSet.

LLM-as-a-Coauthor: The Challenges of Detecting LLM-Human Mixcase