Language models (LMs) are increasingly used as simulacra for people, yet their ability to match the distribution of views of a specific demographic group and be \textit{distributionally aligned} remains uncertain. This notion of distributional alignment is complex, as there is significant variation in the types of attributes that are simulated. Prior works have underexplored the role of three critical variables -- the question domain, steering method, and distribution expression method -- which motivates our contribution of a benchmark explicitly addressing these dimensions. We construct a dataset expanding beyond political values, create human baselines for this task, and evaluate the extent to which an LM can align with a particular group's opinion distribution to inform design choices of such simulation systems. Our analysis reveals open problems regarding if, and how, LMs can be used to simulate humans, and that LLMs can more accurately describe the opinion distribution than simulate such distributions.

翻译：语言模型（LMs）日益被用作人类的模拟体，但其能否匹配特定人口群体的观点分布并实现“分布对齐”仍不确定。这种分布对齐的概念是复杂的，因为被模拟的属性类型存在显著差异。先前的研究对三个关键变量——问题领域、引导方法和分布表达方法——的作用探索不足，这促使我们构建一个明确针对这些维度的基准。我们构建了一个超越政治价值观的数据集，为此任务创建了人类基线，并评估了语言模型在多大程度上能与特定群体的意见分布对齐，从而为此类模拟系统的设计决策提供参考。我们的分析揭示了关于语言模型是否以及如何能用于模拟人类的开放性问题，并发现大型语言模型在描述意见分布方面比模拟此类分布更为准确。