@inproceedings{rezaei-blanco-2025-making,
    title = "Making Language Models Robust Against Negation",
    author = "Rezaei, MohammadHossein  and
      Blanco, Eduardo",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-long.413/",
    pages = "8123--8142",
    ISBN = "979-8-89176-189-6",
    abstract = "Negation has been a long-standing challenge for language models.Previous studies have shown that they struggle with negation in many natural language understanding tasks.In this work, we propose a self-supervised method to make language models more robust against negation.We introduce a novel task, Next Sentence Polarity Prediction (NSPP), and a variation of the Next Sentence Prediction (NSP) task.We show that BERT and RoBERTa further pre-trained on our tasks outperform the off-the-shelf versions on nine negation-related benchmarks.Most notably, our pre-training tasks yield between 1.8{\%} and 9.1{\%} improvement on CondaQA, a large question-answering corpus requiring reasoning over negation."
}