Large language models exhibit societal biases associated with demographic
information, including race, gender, and others. Endowing such language models
with personalities based on demographic data can enable generating opinions
that align with those of humans. Building on this idea, we propose "random
silicon sampling," a method to emulate the opinions of the human population
sub-group. Our study analyzed 1) a language model that generates the survey
responses that correspond with a human group based solely on its demographic
distribution and 2) the applicability of our methodology across various
demographic subgroups and thematic questions. Through random silicon sampling
and using only group-level demographic information, we discovered that language
models can generate response distributions that are remarkably similar to the
actual U.S. public opinion polls. Moreover, we found that the replicability of
language models varies depending on the demographic group and topic of the
question, and this can be attributed to inherent societal biases in the models.
Our findings demonstrate the feasibility of mirroring a group's opinion using
only demographic distribution and elucidate the effect of social biases in
language models on such simulations.

通过使用基于人口分布的人口组别信息，我们研究了语言模型在生成与人群相符合的调查回复方面的可行性，并揭示了语言模型中的社会偏见对这类模拟的影响。