标题： 多语言机器生成文本中的作者归属 显示英文标题

标题： Authorship Attribution in Multilingual Machine-Generated Texts

摘要： 随着大型语言模型（LLMs）达到了类似人类的流利度和连贯性，区分机器生成文本（MGT）和人类撰写的内容变得越来越困难。 尽管早期的MGT检测工作集中在二元分类上，但LLMs不断增长的多样性和广泛的应用要求一种更细致但更具挑战性的作者归属（AA），即能够准确识别文本背后的生成器（LLM或人类）。 然而，目前的AA仍然局限于单一语言环境，英语是最常被研究的语言，忽视了现代LLMs的多语言特性和使用情况。 在本工作中，我们引入了多语言作者归属的问题，涉及将文本归因于不同语言中的人类或多个LLM生成器。 专注于18种语言——涵盖多种语言家族和书写系统——以及8种生成器（7个LLMs和一个由人类撰写的类别），我们研究了单一语言AA方法的多语言适用性、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，虽然某些单一语言AA方法可以适应多语言环境，但仍存在显著的限制和挑战，特别是在跨不同语言家族迁移时，这突显了多语言AA的复杂性，并强调了需要更稳健的方法以更好地匹配现实场景。 显示英文摘要

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, 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.