AGENTIC-SYSTEMSApril 3, 20261 min read18 views

Are they human? Detecting large language models by probing human memory constraints

Researchers explore a novel approach to distinguish LLMs from humans by probing cognitive constraints like working memory. The study shows that even when instructed to mimic humans, AI models can be detected because they lack the specific patterns of human cognitive limitations.

Computer Science > Computation and Language

Title:Are they human? Detecting large language models by probing human memory constraints

Abstract:The validity of online behavioral research relies on study participants being human rather than machine. In the past, it was possible to detect machines by posing simple challenges that were easily solved by humans but not by machines. General-purpose agents based on large language models (LLMs) can now solve many of these challenges, threatening the validity of online behavioral research. Here we explore the idea of detecting humanness by using tasks that machines can solve too well to be human. Specifically, we probe for the existence of an established human cognitive constraint: limited working memory capacity. We show that cognitive modeling on a standard serial recall task can be used to distinguish online participants from LLMs even when the latter are specifically instructed to mimic human working memory constraints. Our results demonstrate that it is viable to use well-established cognitive phenomena to distinguish LLMs from humans.

Source: arXiv cs.AI Recent

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