Analysis of the Neglect-Zero Effect in Large Language Models

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