Abstract
Capabilities of large language models to generate multilingual coherent text have continuously enhanced in recent years, which opens concerns about their potential misuse. Previous research has shown that they can be misused for generation of personalized disinformation in multiple languages. It has also been observed that personalization negatively affects detectability of machine-generated texts; however, this has been studied in the English language only. In this work, we examine this phenomenon across 10 languages, while we focus not only on potential misuse of personalization capabilities, but also on potential benefits they offer. Overall, we cover 1080 combinations of various personalization aspects in the prompts, for which the texts are generated by 16 distinct language models (17,280 texts in total). Our results indicate that there are differences in personalization quality of the generated texts when targeting demographic groups and when targeting social-media platforms across languages. Personalization towards platforms affects detectability of the generated texts in a higher scale, especially in English, where the personalization quality is the highest.
Abstract (translated)
近年来，大型语言模型生成多语种连贯文本的能力不断提升，这引发了对其潜在误用的担忧。先前的研究表明，它们可以被用于多种语言中制造个性化虚假信息。还观察到，个性化会降低机器生成文本的可检测性；然而，这一现象仅在英语中得到了研究。在这项工作中，我们在10种不同的语言环境中考察了这种现象，并不仅关注个性化能力可能带来的潜在误用问题，也探讨它们所提供的潜在益处。总体而言，我们覆盖了各种提示中的个性化方面的1080种组合，这些文本由16个不同的语言模型生成（总计17,280篇）。我们的研究结果表明，在针对不同的人口群体和社交媒体平台时，所生成文本的个性化质量在不同语言中存在差异。面向平台进行个性化尤其会对生成文本的可检测性产生较大影响，尤其是在英语环境中，其个性化质量最高。
URL
https://arxiv.org/abs/2601.03752
