The Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection shared task in the SemEval-2024 competition aims to tackle the problem of misusing collaborative human-AI writing. Although there are a lot of existing detectors of AI content, they are often designed to give a binary answer and thus may not be suitable for more nuanced problem of finding the boundaries between human-written and machine-generated texts, while hybrid human-AI writing becomes more and more popular. In this paper, we address the boundary detection problem. Particularly, we present a pipeline for augmenting data for supervised fine-tuning of DeBERTaV3. We receive new best MAE score, according to the leaderboard of the competition, with this pipeline.

翻译：SemEval-2024 竞赛中的多生成器、多领域、多语言黑盒机器生成文本检测共享任务旨在解决人机协作写作被滥用的问题。尽管现有大量 AI 内容检测器，但它们通常仅提供二元判断，因此可能不适用于寻找人类撰写与机器生成文本之间边界的更细微问题——而随着人机混合写作日益普及，这一需求愈发迫切。本文聚焦边界检测问题，提出了一种用于 DeBERTaV3 监督微调的数据增强流水线。根据竞赛排行榜结果，该流水线取得了新的最优平均绝对误差（MAE）分数。