发现大模型不忽视空集逻辑，挑战人类认知偏见。
Analysis of the Neglect-Zero Effect in Large Language Models
- 用结构启动范式强迫模型关注空集情况
- 模型在空集推理中表现与正常推理无异
- 适合研究语言模型认知机制的学者
我们研究大语言模型（LLMs）的语言处理是否类似人类认知过程，聚焦于一种称为‘忽略零效应’的人类认知偏差。该效应指人们倾向于忽略‘零模型’——即通过空集使命题在逻辑上平凡成立的情况。本文关注由该效应驱动的两类推理，并通过对比不涉及此效应的推理，考察模型行为。采用基于结构启动的实验范式，通过前置句（提示）诱导模型关注零模型，并分析其在目标句中的反应。结果显示，在所测试的模型中，忽略零效应并未出现。代码已公开于https://github.com/ynklab/neglect_zero。
原文摘要 · Abstract (English)
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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