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Benchmarking Large Language Models for Detecting Satirical Code-Mixed Sentences

Răzvan-Alexandru Smădu, Ioana Buhnila, Daniela Tanase, Dumitru-Clementin Cercel, Florin Pop, Mihaela-Claudia Cercel · 2026 · arXiv

DOI: 10.48550/arXiv.2610.29907 · open-access PDF

Chinese

代码交换是一种自然的语言现象,但其对高层次推理任务的影响在罗马尼亚语等资源不足的语言中仍然很大程度上未被探索。自然语言处理任务,如语检测,在很大程度上依赖于细微的文化和语法线索,而大型语言模型难以捕捉。在这篇论文中,我们研究了四种方法(即随机替换,短语翻译,依赖分析和大型语言模型)来生成合成代码混合的罗马尼亚语句子,包括英语,德语,法语和意大利语。

Machine translation (Meta M2M100); the English original follows.

Abstract

Code-switching is a natural linguistic phenomenon, yet its impact on high-level reasoning tasks remains largely unexplored in under-resourced languages like Romanian. Natural language processing tasks, such as satire detection, rely heavily on nuanced cultural and syntactic cues that large language models struggle to capture. In this paper, we investigate four methods (i.e., random replacement, phrase translation, dependency parsing, and large language models) for generating synthetic code-mixed Romanian sentences with English, German, French, and Italian. We start with an existing dataset for

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