This paper presents the MasonTigers entry to the SemEval-2024 Task 8 - Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection. The task encompasses Binary Human-Written vs. Machine-Generated Text Classification (Track A), Multi-Way Machine-Generated Text Classification (Track B), and Human-Machine Mixed Text Detection (Track C). Our best performing approaches utilize mainly the ensemble of discriminator transformer models along with sentence transformer and statistical machine learning approaches in specific cases. Moreover, zero-shot prompting and fine-tuning of FLAN-T5 are used for Track A and B.

翻译：本文介绍MasonTigers团队参与SemEval-2024任务8——“多生成器、多领域、多语言黑箱机器生成文本检测”的成果。该任务涵盖二元人工编写与机器生成文本分类（轨道A）、多类别机器生成文本分类（轨道B）以及人机混合文本检测（轨道C）。我们的最优方法主要采用判别式Transformer模型的集成，并在特定场景中结合句子Transformer与统计机器学习方法。此外，针对轨道A和B，我们使用了FLAN-T5的零样本提示与微调技术。