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.

翻译：大语言模型表现出与种族、性别等人口统计学信息相关的社会偏见。赋予此类语言模型基于人口统计数据的人格特征，可以生成与人类观点一致的输出。基于这一思想，我们提出“随机硅采样”方法，用于模拟人类子群体的意见。本研究分析了：1）仅基于群体人口分布即可生成与人类群体调查响应一致的语言模型；2）该方法在不同人口统计子群体及主题性问题中的适用性。通过随机硅采样并仅使用群体层面的人口统计学信息，我们发现语言模型能够生成与美国实际民意调查结果高度相似的响应分布。此外，我们发现语言模型的复现性因人口统计群体和问题主题而异，这归因于模型中固有的社会偏见。研究结果证明了仅利用人口分布即可镜像群体意见的可行性，并阐明了语言模型中的社会偏见对此类模拟的影响。