We study adaptive querying for learning user-dependent quantities of interest, such as responses to held-out items and psychometric indicators, within tight question budgets. Classical Bayesian design and computerized adaptive testing typically rely on restrictive parametric assumptions or expensive posterior approximations, limiting their use in heterogeneous, high-dimensional, and cold-start settings. We introduce a persona-induced latent variable model that represents a user's state through membership in a finite dictionary of AI personas, each offering response distributions produced by a large language model. This yields expressive priors with closed-form posterior updates and efficient finite-mixture predictions, enabling scalable Bayesian design for sequential item selection. Experiments on synthetic data and WorldValuesBench demonstrate that persona-based posteriors deliver accurate probabilistic predictions and an interpretable adaptive elicitation pipeline.

翻译：我们研究在有限提问预算下，针对用户依赖的感兴趣量（如对保留项目的响应和心理测量指标）进行自适应查询。经典的贝叶斯设计和计算机化自适应测试通常依赖于严格的参数假设或昂贵的后验近似，这限制了它们在异质性、高维和冷启动场景中的应用。我们引入一种由人格诱导的潜在变量模型，该模型通过用户在有限AI人格字典中的成员身份来表示其状态，其中每个人格由大语言模型生成响应分布。这产生了具有闭合形式后验更新和高效有限混合预测的表达性先验，从而实现了可扩展的贝叶斯设计以进行顺序项目选择。在合成数据和WorldValuesBench上的实验表明，基于人格的后验能够提供准确的概率预测以及可解释的自适应引出流程。