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Benchmarking Large Language Models for Detecting Satirical Code-Mixed Sentences

Răzvan-Alexandru Smădu, Ioana Buhnila, Daniela Tănase, Dumitru-Clementin Cercel, Florin Pop, Mihaela-Claudia Cercel · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29907 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

ルーマニア語などのリソース不足の言語では、コードエクスイッチングは自然言語的現象ですが、その高レベルの推理タスクへの影響は、ルーマニア語などのリソース不足の言語ではほとんど未知のままです。 サティア検出などの自然言語処理タスクは、自然言語検出は、豊富にサティア語の検出のための既存のデータセットから始まり、ルーマニア語のニュース記事からまとめられ、人によって文レベルで注釈された4つの方法(すなわち、ランダム代替、フレーズ翻訳、依存性サンプル、および大型言語モデル)を生成するための合成コード混合ルーマニア語の文句を英語、ドイツ語、フランス

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 satire detection, compiled from Romanian news articles and annotated at the sentence level by humans, and augment it with over 600k samples, namely CS-SeLeRoSa. We provide an in-depth analysis of the dataset generation process. Then, we evaluate several multilingual large language models across zero-shot, chain-of-thought, and fine-tuning regimes on both the synthetic corpus and a human-adjudicated gold benchmark, namely CS-SeLeRoSa-Gold. While older open models suffer substantial degradation (dropping to 37%-55% macro-F1), newer models like Gemma 4 31B achieve 68.1% zero-shot, rivaling frontier models like GPT-5 mini (69.5%). Furthermore, fine-tuning significantly reduces formatting errors and improves the results, increasing Gemma 4 31B to 77.5% on CS-SeLeRoSa and 72.6% on the gold standard, outperforming frontier zero-shot baselines.

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Topics: Computer science, Natural language processing, Artificial intelligence, Language model, Sentence, Phrase

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