@inproceedings{yanaka-yamamoto-2026-revisiting,
    title = "Revisiting the Systematicity in Negation in the Era of In-Context Learning",
    author = "Yanaka, Hitomi  and
      Yamamoto, Taisei",
    editor = "Yanaka, Hitomi  and
      Abzianidze, Lasha",
    booktitle = "Proceedings of the 6th Workshop on Natural Language Meets Logic and Machine Learning ({NALOMA})",
    month = aug,
    year = "2026",
    address = "Prague, Czechia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-naloma/2026.naloma-1.1/",
    pages = "1--8",
    ISBN = "979-8-89176-389-0",
    abstract = "Understanding the meaning of negated sentences remains one of the challenges for language models even in the era of large language models (LLMs). We analyze systematicity regarding LLM understanding of negation from two perspectives: behavioral systematicity and representational systematicity. For behavioral systematicity, we confirm that through demonstrations and in-context learning, LLMs can recognize negation expressions and negation scope within sentences to some extent, but they fail to achieve perfect performance. In particular, the difficulty of the negation scope recognition for models varies depending on the output format. For representational systematicity, we analyze the extent to which LLMs can construct function vectors related to the tasks necessary for understanding negation from in-context examples. The experiments suggest that while models can compose the function vectors for negation tasks, extracting the function vector for recognizing scope is more challenging."
}