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 systematically generated, high-quality datasets for training. To address this issue, we propose five novel data augmentation frameworks for synthetic user dialogue generation through a structured prompting approach, reducing the costs associated with traditional data collection methods. Our proposed method yields 14 new dialogue datasets, which we benchmark against seven MGT detection models. The results demonstrate improved generalization performance when utilizing a mixed dataset produced by our proposed augmentation framework. Furthermore, 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. We also benchmark online detection performance with limited chat history on our frameworks. Our open-source datasets can be downloaded fromthis https URL.
View on arXiv@article{li2025_2503.15044,
title={ SPADE: Systematic Prompt Framework for Automated Dialogue Expansion in Machine-Generated Text Detection },
author={ Haoyi Li and Angela Yifei Yuan and Soyeon Caren Han and Christopher Leckie },
journal={arXiv preprint arXiv:2503.15044},
year={ 2025 }
}