@inproceedings{wang-etal-2026-commonsense,
    title = "Commonsense Knowledge with Negation: A Resource to Enhance Negation Understanding",
    author = "Wang, Zijie  and
      Rezaei, MohammadHossein  and
      Rashid, Farzana  and
      Blanco, Eduardo",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-form-platform/2026.findings-acl.578/",
    pages = "11915--11934",
    ISBN = "979-8-89176-395-1",
    abstract = "Negation is a common and important semantic feature in natural language, yet Large Language Models (LLMs) struggle when negation is involved in natural language understanding tasks. Commonsense knowledge, on the other hand, despite being a well-studied topic, lacks investigations involving negation. In this work, we show that commonsense knowledge with negation is challenging for models to understand. We present a novel approach to automatically augment existing commonsense knowledge corpora with negation, yielding two new corpora containing over 2M triples with if-then relations. In addition, pre-training LLMs on our corpora benefits negation understanding."
}