Will the Real Linda Please Stand up...to Large Language Models?
  Examining the Representativeness Heuristic in LLMs
Pengda Wang Zilin Xiao Hanjie Chen Frederick L. Oswald
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 or
statistical evidence. This work investigates the impact of the
representativeness heuristic on LLM reasoning. We created REHEAT
(Representativeness Heuristic AI Testing), a dataset containing a series of
problems spanning six common types of representativeness heuristics.
Experiments reveal that four LLMs applied to REHEAT all exhibited
representativeness heuristic biases. We further identify that the model's
reasoning steps are often incorrectly based on a stereotype rather than the
problem's description. Interestingly, the performance improves when adding a
hint in the prompt to remind the model of using its knowledge. This suggests
the uniqueness of the representativeness heuristic compared to traditional
biases. It can occur even when LLMs possess the correct knowledge while failing
in a cognitive trap. This highlights the importance of future research focusing
on the representativeness heuristic in model reasoning and decision-making and
on developing solutions to address it.