Large language models (LLMs) are increasingly used to simulate human opinions and survey responses, but their ability to reproduce population responses across cultures remains limited. Existing persona-based prompting methods typically rely on sociodemographic or personality traits, which are only indirect proxies for the values that shape human responses. We propose a value-based persona construction method that derives textual descriptors from survey responses capturing core cultural dimensions. By sampling value profiles from target populations and aggregating LLM responses across personas, we obtain population-level predictions grounded in observed value distributions. We further introduce a calibration procedure that improves response diversity while preserving estimated opinions. We show that our approach reduces prediction error across countries, with the largest improvements observed in underrepresented populations. This substantially narrows the performance gap between countries aligned with dominant LLM priors and those that are less represented in training data, while also yielding response distributions that closely match human diversity.

翻译：大型语言模型（LLMs）在模拟人类观点和调查响应方面应用日益广泛，但其跨文化再现群体响应的能力仍存在局限。现有基于人格的提示方法通常依赖社会人口统计学或人格特质，而这些仅是塑造人类响应的价值观的间接代理指标。我们提出一种基于价值观的人格构建方法，从捕捉核心文化维度的调查响应中提取文本描述符。通过从目标群体中采样价值观分布，并聚合LLMs在不同人格上的响应，我们获得了基于观测价值观分布的群体级预测。进一步引入校准流程，在保持估计观点的同时提升响应多样性。研究表明，我们的方法降低了各国的预测误差，在代表性不足的群体中改进效果最为显著。这显著缩小了与主流LLM先验对齐的国家与训练数据中代表性不足国家之间的性能差距，同时生成的响应分布与人类多样性高度匹配。