  This study introduces a taxonomy of stereotype content in contemporary large
language models (LLMs). We prompt ChatGPT 3.5, Llama 3, and Mixtral 8x7B, three
powerful and widely used LLMs, for the characteristics associated with 87
social categories (e.g., gender, race, occupations). We identify 14 stereotype
dimensions (e.g., Morality, Ability, Health, Beliefs, Emotions), accounting for
~90% of LLM stereotype associations. Warmth and Competence facets were the most
frequent content, but all other dimensions were significantly prevalent.
Stereotypes were more positive in LLMs (vs. humans), but there was significant
variability across categories and dimensions. Finally, the taxonomy predicted
the LLMs' internal evaluations of social categories (e.g., how
positively/negatively the categories were represented), supporting the
relevance of a multidimensional taxonomy for characterizing LLM stereotypes.
Our findings suggest that high-dimensional human stereotypes are reflected in
LLMs and must be considered in AI auditing and debiasing to minimize
unidentified harms from reliance in low-dimensional views of bias in LLMs.
