LLMs Do Not Have Human-Like Working Memory

Abstract

Human working memory is an active cognitive system that enables not only the temporary storage of information but also its processing and utilization. Without working memory, individuals may produce unreal conversations, exhibit self-contradiction, and struggle with tasks requiring mental reasoning. In this paper, we demonstrate that Large Language Models (LLMs) lack this human-like cognitive ability, posing a significant challenge to achieving artificial general intelligence. We validate this claim through three experiments: (1) Number Guessing Game, (2) Yes or No Game, and (3) Math Magic. Experimental results on several model families indicate that current LLMs fail to exhibit human-like cognitive behaviors in these scenarios. By highlighting this limitation, we aim to encourage further research in developing LLMs with improved working memory capabilities.

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