Will the Real Linda Please Stand up...to Large Language Models? Examining the Representativeness Heuristic in LLMs

arXiv:2404.01461v1 Announce Type: new
Abstract: Although large language models (LLMs) have demonstrated remarkable proficiency in understanding text and generating human-like text, they may exhibit biases acquired from training data in doing so. Specifically, LLMs may be susceptible to a common cognitive trap in human decision-making called the representativeness heuristic. This is a concept in psychology that refers to judging the likelihood of an event based on how closely it resembles a well-known prototype or typical example versus considering broader facts …

abstract acquired arxiv biases cognitive cs.cl cs.hc data human human-like language language models large language large language models llms text training training data type understanding will