大型语言模型（LLM）是否了解你的社区？审计LLM先验以用于社区层面的流动性预测与结构对齐

中文摘要 AI 辅助

人类流动性是城市规划、交通、公共卫生和应急响应的核心，但细粒度轨迹数据通常具有专有性、受限制且涉及隐私敏感。大型语言模型（LLM）通过生成合理的流动性轨迹并预测个体移动，提供了一种潜在替代方案，但其推断社区层面总体流动性的能力仍不明确。我们使用匿名化的Cuebiq数据，在四个美国大都市区对零样本LLM进行人口普查区块组层面的流动性预测评估，构建了点层面、轨迹层面和时间层面的流动性结果，并搭配社会人口统计学与建成环境预测因子。我们将LLM预测结果与监督基线进行比较，并引入方向对齐分析以测试LLM隐含的预测因子效应是否与经验性普通最小二乘法（OLS）及Jonckheere-Terpstra趋势一致。监督模型的平均准确率为0.580，最佳LLM的准确率为0.435；空间范围结果表现出最强的可预测性，但也存在最大的LLM与基线的差距。方向分析显示，LLM通常依赖于跨结果和城市保持相似的粗粒度、稳定的预测因子层面先验，包括对受保护群体预测因子的不对称处理。总体而言，LLM可从城市语境中部分恢复总体流动性模式，但未经审计其经验对齐性和潜在偏差时，不应将其预测视为具有结构基础的结果。

英文摘要

Human mobility is central to urban planning, transportation, public health, and emergency response, yet fine-grained trajectory data are often proprietary, restricted, and privacy-sensitive. Large language models (LLMs) offer a potential alternative by generating plausible mobility traces and predicting individual movement, but their ability to infer aggregate neighborhood-level mobility remains unclear. We evaluate zero-shot LLMs on Census Block Group-level mobility prediction across four U.S. metropolitan areas using anonymized Cuebiq data to construct point-level, trajectory-level, and temporal mobility outcomes, paired with sociodemographic and built-environment predictors. We compare LLM predictions with supervised baselines and introduce a directional alignment analysis to test whether LLM-implied predictor effects agree with empirical OLS and Jonckheere-Terpstra trends. Supervised models achieve 0.580 average accuracy, compared with 0.435 for the best LLM, with spatial extent outcomes showing the strongest predictability but also the largest LLM-baseline gaps. Directional analysis shows that LLMs often rely on coarse, stable predictor-level priors that remain similar across outcomes and cities, including asymmetric treatment of protected-group predictors. Overall, LLMs can partially recover aggregate mobility patterns from urban context, but their predictions should not be treated as structurally grounded without auditing empirical alignment and potential bias.

发表机构

• University of Maryland（马里兰大学）

机构由 AI 辅助整理，请以论文原文为准。