  One uniquely human trait is our inability to be random. We see and produce
patterns where there should not be any and we do so in a predictable way. LLMs
are supplied with human data and prone to human biases. In this work, we
explore how LLMs approach randomness and where and how they fail through the
lens of the well studied phenomena of generating binary random sequences. We
find that GPT 4 and Llama 3 exhibit and exacerbate nearly every human bias we
test in this context, but GPT 3.5 exhibits more random behavior. This dichotomy
of randomness or humaness is proposed as a fundamental question of LLMs and
that either behavior may be useful in different circumstances.
