@inproceedings{hyben-etal-2026-multicw,
    title = "{M}ulti{CW}: A Large-Scale Balanced Benchmark Dataset for Training Robust Check-Worthiness Detection Models",
    author = "Hyben, Martin  and
      Kula, Sebastian  and
      Cegin, Jan  and
      Simko, Jakub  and
      Srba, Ivan  and
      Moro, Robert",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.194/",
    pages = "3737--3754",
    ISBN = "979-8-89176-386-9",
    abstract = "Large language models (LLMs) are beginning to reshape how media professionals verify information, yet automated support for detecting check-worthy claims{---}a key step in the fact-checking process{---}remains limited. We introduce the Multi-Check-Worthy (MultiCW) dataset, a balanced multilingual benchmark for check-worthy claim detection spanning 16 languages, six topical domains, and two writing styles. It consists of 123,722 samples, evenly distributed between noisy (informal) and structured (formal) texts, with balanced representation of check-worthy and non-check-worthy classes across all languages. To probe robustness, we also introduce an equally balanced out-of-distribution evaluation set of 27,761 samples in 4 additional languages. To provide baselines, we benchmark three common fine-tuned multilingual transformers against a diverse set of 15 commercial and open LLMs under zero-shot settings. Our findings show that fine-tuned models consistently outperform zero-shot LLMs on claim classification and show strong out-of-distribution generalization across languages, domains, and styles. MultiCW provides a rigorous multilingual resource for advancing automated fact-checking and enables systematic comparisons between fine-tuned models and cutting-edge LLMs on the check-worthy claim detection task."
}