跨10语言测试大模型个性化生成，发现平台定向更难检测
Evaluation of Multilingual LLMs Personalized Text Generation Capabilities Targeting Groups and Social-Media Platforms
- 测试16个模型在1080种个性化组合下的文本生成能力
- 平台定向个人化使文本检测难度显著上升，英语最明显
- 既揭示风险也展示个性化生成的潜在价值
近年来大型语言模型生成多语言连贯文本的能力持续提升，引发对其潜在滥用的担忧。已有研究显示，这些模型可被用于生成多语言个性化虚假信息。此前发现，个性化会降低机器生成文本的可检测性，但仅限于英语。本研究在10种语言中考察该现象，不仅关注个性化可能带来的滥用风险，也探讨其潜在益处。我们共测试了1080种个性化提示组合，由16个不同语言模型生成共计17,280篇文本。结果表明，在针对不同人口群体和社交平台时，生成文本的个性化质量存在语言差异。针对社交平台的个性化对文本可检测性的影响更大，尤其在英语中，个性化质量最高。
原文摘要 · Abstract (English)
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.
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