多语言机器生成文本的作者归属
Authorship Attribution in Multilingual Machine-Generated Texts
AI总结：
提出多语言作者归属问题，研究单语言方法在18种语言和8个生成器上的跨语言迁移能力，发现显著局限。
摘要：
随着大型语言模型（LLM）达到类人的流畅性和连贯性，区分机器生成文本（MGT）与人类撰写的内容变得越来越困难。虽然MGT检测的早期工作侧重于二元分类，但LLM的不断发展和多样性需要更细粒度且更具挑战性的作者归属（AA），即能够识别文本背后的确切生成器（LLM或人类）。然而，目前AA仍局限于单语言环境，其中英语是研究最多的语言，忽视了现代LLM的多语言性质和使用。在这项工作中，我们引入了多语言作者归属问题，涉及将文本归因于跨多种语言的人类或多个LLM生成器。聚焦于18种语言——涵盖多个语系和书写系统——以及8个生成器（7个LLM和人类撰写类别），我们研究了单语言AA方法在多语言环境中的适用性，包括其跨语言迁移能力，以及生成器对归属性能的影响。我们的结果表明，虽然某些单语言AA方法可以适应多语言环境，但仍然存在显著的局限性和挑战，特别是在跨不同语系迁移时，这凸显了多语言AA的复杂性以及需要更稳健的方法以更好地匹配现实场景。
英文摘要：
As Large Language Models (LLMs) have reached human-like fluency and coherence, distinguishing machine-generated text (MGT) from human-written content becomes increasingly difficult. While early efforts in MGT detection have focused on binary classification, the growing landscape and diversity of LLMs require a more fine-grained yet challenging authorship attribution (AA), i.e., being able to identify the precise generator (LLM or human) behind a text. However, AA remains nowadays confined to a monolingual setting, with English being the most investigated one, overlooking the multilingual nature and usage of modern LLMs. In this work, we introduce the problem of Multilingual Authorship Attribution, which involves attributing texts to human or multiple LLM generators across diverse languages. Focusing on 18 languages -- covering multiple families and writing scripts -- and 8 generators (7 LLMs and the human-authored class), we investigate the multilingual suitability of monolingual AA methods in terms of their cross-lingual transferability, and the impact of generators on attribution performance. Our results reveal that while certain monolingual AA methods can be adapted to multilingual settings, significant limitations and challenges remain, particularly in transferring across diverse language families, underscoring the complexity of multilingual AA and the need for more robust approaches to better match real-world scenarios.
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