Analysis of the Neglect-Zero Effect in Large Language Models

• URL: http://arxiv.org/abs/2606.05864v1
• Date: Thu, 04 Jun 2026 08:39:39 GMT
• Title: Analysis of the Neglect-Zero Effect in Large Language Models
• Abstract summary: We investigate the extent to which the language processing of LLMs resembles human cognitive processes.<n>We focus on two types of inferences driven by the neglect-zero effect.<n>The results suggest that the neglect-zero effect may not occur in the LLMs analyzed in this study.
• Score: 17.164287400700815
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 https://github.com/ynklab/neglect_zero

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