  As Large Language Models (LLMs) ascend in popularity, offering information
with unprecedented convenience compared to traditional search engines, we delve
into the intriguing possibility that a new, singular perspective is being
propagated. We call this the "Silent Curriculum," where our focus shifts
towards a particularly impressionable demographic: children, who are drawn to
the ease and immediacy of acquiring knowledge through these digital oracles. In
this exploration, we delve into the sociocultural ramifications of LLMs, which,
through their nuanced responses, may be subtly etching their own stereotypes,
an algorithmic or AI monoculture. We hypothesize that the convergence of
pre-training data, fine-tuning datasets, and analogous guardrails across models
may have birthed a distinct cultural lens. We unpack this concept through a
short experiment navigating children's storytelling, occupational-ethnic
biases, and self-diagnosed annotations, to find that there exists strong cosine
similarity (0.87) of biases across these models, suggesting a similar
perspective of ethnic stereotypes in occupations. This paper invites a
reimagining of LLMs' societal role, especially as the new information
gatekeepers, advocating for a paradigm shift towards diversity-rich landscapes
over unintended monocultures.
