@inproceedings{li-etal-2025-spade,
    title = "{SPADE}: Structured Prompting Augmentation for Dialogue Enhancement in Machine-Generated Text Detection",
    author = "Li, Haoyi  and
      Yuan, Angela  and
      Han, Soyeon  and
      Leckie, Chirstopher",
    editor = "Derczynski, Leon  and
      Novikova, Jekaterina  and
      Chen, Muhao",
    booktitle = "Proceedings of the The First Workshop on LLM Security (LLMSEC)",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.llmsec-1.11/",
    pages = "142--167",
    ISBN = "979-8-89176-279-4",
    abstract = "The increasing capability of large language models (LLMs) to generate synthetic content has heightened concerns about their misuse, driving the development of Machine-Generated Text (MGT) detection models. However, these detectors face significant challenges due to the lack of high-quality synthetic datasets for training. To address this issue, we propose SPADE, a structured framework for detecting synthetic dialogues using prompt-based adversarial samples. Our proposed methods yield 14 new dialogue datasets, which we benchmark against eight MGT detection models. The results demonstrate improved generalization performance when utilizing a mixed dataset produced by proposed augmentation frameworks, offering a practical approach to enhancing LLM application security. Considering that real-world agents lack knowledge of future opponent utterances, we simulate online dialogue detection and examine the relationship between chat history length and detection accuracy. Our open-source datasets can be downloaded."
}