首个针对中欧语言的机器生成文本检测基准，解决多语言检测难题。
CEAID: Benchmark of Multilingual Machine-Generated Text Detection Methods for Central European Languages
- 构建中欧语言机器生成文本检测基准，涵盖多领域、多生成器和多语言场景。
- 在中欧语言上，微调后的监督模型性能最佳且抗混淆能力最强。
- 揭示不同训练语言组合的效果差异，为跨语言迁移提供实证依据。
机器生成文本检测作为重要任务，研究主要集中在英语，导致现有检测器对非英语语言几乎无效，完全依赖跨语言迁移。目前仅少数工作关注中欧语言，其迁移能力尚未充分探索。本文首次建立面向该区域的检测方法基准，同时对比不同训练语言组合，识别最优方案。评估覆盖多领域、多生成器及多语言场景，分析各维度差异及检测方法的对抗鲁棒性。结果表明，在中欧语言上，经过微调的监督模型表现最佳，且对文本混淆具有最强抵抗能力。
原文摘要 · Abstract (English)
Machine-generated text detection, as an important task, is predominantly focused on English in research. This makes the existing detectors almost unusable for non-English languages, relying purely on cross-lingual transferability. There exist only a few works focused on any of Central European languages, leaving the transferability towards these languages rather unexplored. We fill this gap by providing the first benchmark of detection methods focused on this region, while also providing comparison of train-languages combinations to identify the best performing ones. We focus on multi-domain, multi-generator, and multilingual evaluation, pinpointing the differences of individual aspects, as well as adversarial robustness of detection methods. Supervised finetuned detectors in the Central European languages are found the most performant in these languages as well as the most resistant against obfuscation.
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