@inproceedings{tanaka-etal-2026-analysis,
    title = "Analysis of the Neglect-Zero Effect in Large Language Models",
    author = "Tanaka, Jin  and
      Matsuoka, Daiki  and
      Kumon, Ryoma  and
      Yanaka, Hitomi",
    editor = "T.Y.S.S., Santosh  and
      Rodriguez, Juan Diego  and
      de Gibert, Ona",
    booktitle = "Proceedings of the 64th Annual Meeting 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/ingest-acl/2026.acl-srw.91/",
    pages = "1052--1064",
    ISBN = "979-8-89176-393-7",
    abstract = "We investigate the extent to which the language processing of LLMs resembles human cognitive processes, focusing on a human cognitive bias called the *neglect-zero effect*. This effect refers to the human tendency to ignore *zero-models*, which are configurations that render a proposition vacuously true by virtue of an empty set. We focus on two types of inferences driven by the neglect-zero effect, and examine how LLMs process these inferences by comparing their behavior with that in an inference that does not involve the neglect-zero effect. For this purpose, we employ a paradigm based on *structural priming*, where recent exposure to a preceding sentence (the *prime*) facilitates the processing of a subsequent sentence (the *target*) due to their structural similarity. We prepare primes to force LLMs to consider the zero-model, and analyze whether they also consider it in the target. The results suggest that the neglect-zero effect may not occur in the LLMs analyzed in this study. Our code is available at https://github.com/ynklab/neglect{\_}zero."
}