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arXiv Computation and Language By R\u{a}zvan-Alexandru Sm\u{a}du, Ioana Buhnila, Daniela Tanase, Dumitru-Clementin Cercel, Florin Pop, Mihaela-Claudia Cercel

Benchmarking Large Language Models for Detecting Satirical Code-Mixed Sentences

Read the original on arXiv Computation and Language →

The paper introduces CS‑SeLeRoSa, a synthetic dataset of over 600k code‑mixed Romanian sentences with English, German, French, and Italian, generated via four methods (random replacement, phrase translation, dependency parsing, and large language models). It evaluates multilingual large language models on satire detection in this dataset and a human‑annotated gold benchmark, CS‑SeLeRoSa‑Gold, showing that newer models like Gemma 4 31B outperform older open models and achieve competitive zero‑shot performance, with fine‑tuning further improving results.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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