@inproceedings{vrabcova-etal-2025-towards,
    title = "Towards the Roots of the Negation Problem: A Multilingual {NLI} Dataset and Model Scaling Analysis",
    author = "Vrabcov{\'a}, Tereza  and
      Kadl{\v{c}}{\'i}k, Marek  and
      Sojka, Petr  and
      {\v{S}}tef{\'a}nik, Michal  and
      Spiegel, Michal",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1391/",
    doi = "10.18653/v1/2025.findings-emnlp.1391",
    pages = "25537--25551",
    ISBN = "979-8-89176-335-7",
    abstract = "Negations are key to determining sentence meaning, making them essential for logical reasoning. Despite their importance, negations pose a substantial challenge for large language models (LLMs) and remain underexplored.We constructed and published two new textual entailment datasets NoFEVER-ML and NoSNLI-ML in four languages (English, Czech, German, and Ukrainian) with $\textit{paired}$ examples differing in negation. It allows investigation of the root causes of the negation problem and its exemplification: how popular LLM model properties and language impact their inability to handle negation correctly.Contrary to previous work, we show that increasing the model size may improve the models' ability to handle negations. Furthermore, we find that both the models' reasoning accuracy and robustness to negation are language-dependent and that the length and explicitness of the premise have an impact on robustness. We observe higher accuracy in languages with relatively fixed word order like English, compared to those with greater flexibility like Czech and German.Our entailment datasets pave the way to further research for explanation and exemplification of the negation problem, minimization of LLM hallucinations, and improvement of LLM reasoning in multilingual settings."
}