随机硅抽样：利用基于群体级人口统计信息的大语言模型模拟人类亚群体观点
Random Silicon Sampling: Simulating Human Sub-Population Opinion Using a Large Language Model Based on Group-Level Demographic Information
AI总结：
提出“随机硅抽样”方法，仅依据群体级人口统计分布让大语言模型生成调查回答，发现其可高度近似美国真实民调结果，并揭示社会偏见导致不同群体与主题间的可复制性差异。
摘要：
大语言模型表现出与种族、性别等人口统计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，可使其生成与人类观点相一致的看法。基于这一思想，我们提出“随机硅抽样”（random silicon sampling）方法，用以模拟人类人口亚群体的观点。本研究分析了：1）一种仅依据某一人类群体的人口统计分布即可生成与其相对应的调查回答的语言模型；2）该方法在不同人口统计亚群体和各类主题问题上的适用性。通过随机硅抽样，并且仅使用群体级人口统计信息，我们发现语言模型能够生成与美国真实民意调查极为相似的回答分布。此外，我们还发现语言模型的可复制性会因人口统计群体和问题主题的不同而变化，这可归因于模型内在的社会偏见。我们的研究结果证明了仅利用人口统计分布来镜像反映某一群体观点的可行性，并阐明了语言模型中的社会偏见对此类模拟的影响。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
