  Of the many commercial and scientific opportunities provided by large
language models (LLMs; including Open AI's ChatGPT, Meta's LLaMA, and
Anthropic's Claude), one of the more intriguing applications has been the
simulation of human behavior and opinion. LLMs have been used to generate human
simulcra to serve as experimental participants, survey respondents, or other
independent agents, with outcomes that often closely parallel the observed
behavior of their genuine human counterparts. Here, we specifically consider
the feasibility of using LLMs to estimate subpopulation representative models
(SRMs). SRMs could provide an alternate or complementary way to measure public
opinion among demographic, geographic, or political segments of the population.
However, the introduction of new technology to the socio-technical
infrastructure does not come without risk. We provide an overview of behavior
elicitation techniques for LLMs, and a survey of existing SRM implementations.
We offer frameworks for the analysis, development, and practical implementation
of LLMs as SRMs, consider potential risks, and suggest directions for future
work.
