  Large language models are becoming increasingly integrated into our lives.
Hence, it is important to understand the biases present in their outputs in
order to avoid perpetuating harmful stereotypes, which originate in our own
flawed ways of thinking. This challenge requires developing new benchmarks and
methods for quantifying affective and semantic bias, keeping in mind that LLMs
act as psycho-social mirrors that reflect the views and tendencies that are
prevalent in society. One such tendency that has harmful negative effects is
the global phenomenon of anxiety toward math and STEM subjects. Here, we
investigate perceptions of math and STEM fields provided by cutting-edge
language models, namely GPT-3, Chat-GPT, and GPT-4, by applying an approach
from network science and cognitive psychology. Specifically, we use behavioral
forma mentis networks (BFMNs) to understand how these LLMs frame math and STEM
disciplines in relation to other concepts. We use data obtained by probing the
three LLMs in a language generation task that has previously been applied to
humans. Our findings indicate that LLMs have an overall negative perception of
math and STEM fields, with math being perceived most negatively. We observe
significant differences across the three LLMs. We observe that newer versions
(i.e. GPT-4) produce richer, more complex perceptions as well as less negative
perceptions compared to older versions and N=159 high-school students. These
findings suggest that advances in the architecture of LLMs may lead to
increasingly less biased models that could even perhaps someday aid in reducing
harmful stereotypes in society rather than perpetuating them.
