The recent development of large language models (LLMs) has spurred discussions about whether LLM-generated "synthetic samples" could complement or replace traditional surveys, considering their training data potentially reflects attitudes and behaviors prevalent in the population. A number of mostly US-based studies have prompted LLMs to mimic survey respondents, with some of them finding that the responses closely match the survey data. However, several contextual factors related to the relationship between the respective target population and LLM training data might affect the generalizability of such findings. In this study, we investigate the extent to which LLMs can estimate public opinion in Germany, using the example of vote choice. We generate a synthetic sample of personas matching the individual characteristics of the 2017 German Longitudinal Election Study respondents. We ask the LLM GPT-3.5 to predict each respondent's vote choice and compare these predictions to the survey-based estimates on the aggregate and subgroup levels. We find that GPT-3.5 does not predict citizens' vote choice accurately, exhibiting a bias towards the Green and Left parties. While the LLM captures the tendencies of "typical" voter subgroups, such as partisans, it misses the multifaceted factors swaying individual voter choices. By examining the LLM-based prediction of voting behavior in a new context, our study contributes to the growing body of research about the conditions under which LLMs can be leveraged for studying public opinion. The findings point to disparities in opinion representation in LLMs and underscore the limitations in applying them for public opinion estimation.

翻译：大型语言模型（LLM）的最新发展引发了关于LLM生成的"合成样本"能否补充或取代传统调查的讨论，因为其训练数据可能反映了人群中普遍存在的态度和行为。许多主要基于美国的研究促使LLM模拟调查受访者，其中部分研究发现LLM的回应与调查数据高度吻合。然而，目标人群与LLM训练数据之间关系的若干情境因素可能影响此类发现的普适性。本研究以投票选择为例，探讨LLM在多大程度上能够估计德国的公众舆论。我们生成了与2017年德国纵向选举研究受访者个体特征相匹配的合成人物样本，要求LLM GPT-3.5预测每位受访者的投票选择，并将这些预测与基于调查的聚合层面及子群体层面的估计结果进行比较。研究发现GPT-3.5无法准确预测公民的投票选择，表现出对绿党和左翼党的偏向。虽然LLM能够捕捉"典型"选民子群体（如党派支持者）的倾向，但未能把握影响个体投票选择的多维度因素。通过在新情境下检验基于LLM的投票行为预测，本研究为不断增长的关于LLM应用于公众舆论研究的条件探讨作出了贡献。研究结果揭示了LLM在意见表征方面存在的差异，并强调了将其应用于公众舆论估计的局限性。