@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 (Volume 4: Student Research Workshop)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-srw.91/",
doi = "10.18653/v1/2026.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 \textit{neglect-zero effect}. This effect refers to the human tendency to ignore \textit{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 \textit{structural priming}, where recent exposure to a preceding sentence (the \textit{prime}) facilitates the processing of a subsequent sentence (the \textit{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 \url{https://github.com/ynklab/neglect_zero}."
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<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.</abstract>
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%0 Conference Proceedings
%T Analysis of the Neglect-Zero Effect in Large Language Models
%A Tanaka, Jin
%A Matsuoka, Daiki
%A Kumon, Ryoma
%A Yanaka, Hitomi
%Y T.Y.S.S., Santosh
%Y Rodriguez, Juan Diego
%Y de Gibert, Ona
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-393-7
%F tanaka-etal-2026-analysis
%X 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.
%R 10.18653/v1/2026.acl-srw.91
%U https://aclanthology.org/2026.acl-srw.91/
%U https://doi.org/10.18653/v1/2026.acl-srw.91
%P 1052-1064
Markdown (Informal)
[Analysis of the Neglect-Zero Effect in Large Language Models](https://aclanthology.org/2026.acl-srw.91/) (Tanaka et al., ACL 2026)
ACL
- Jin Tanaka, Daiki Matsuoka, Ryoma Kumon, and Hitomi Yanaka. 2026. Analysis of the Neglect-Zero Effect in Large Language Models. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 1052–1064, San Diego, California, United States. Association for Computational Linguistics.