发表机构
The University of Melbourne; Shenzhen University（墨尔本大学; 深圳大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过深圳5000名居民的移动数据基准测试，发现社会人口统计属性对LLM下一位置预测无增量价值，而候选地点构建方式对准确率影响显著。
AI 中文摘要
大型语言模型（LLMs）越来越多地被用于个体下一位置预测，而社会人口统计条件化在基于LLM的出行模拟中也很常见。然而，社会人口统计属性的增量预测价值仍不清楚。为直接检验这一贡献，我们将社会人口统计数据与来自5000名深圳居民的被动感知移动数据相关联，构建了一个封闭集基准，其中模型对100个候选目的地进行排序。每个预测实例在保持移动历史、候选地点及所有其他提示内容固定的情况下，分别在有和没有年龄、性别、职业和收入信息的条件下进行评估。结果显示，在四种历史长度下，top-1准确率的配对变化范围从-0.8到+0.5个百分点，属性未带来可检测的增益。当停留历史被隐藏、在替代预测时间、在另外两个LLM以及在同一基准上训练的有监督重排序器中，这一结果保持一致。这一零结果并不反映模型对人口统计信息缺乏响应性，因为置换属性会降低LLM准确率，而正确匹配的属性则不会提高准确率。在反向预测方向上出现了进一步的不对称性，即截断前的移动轨迹以0.708的AUC恢复收入，而社会人口统计属性对下一位置预测贡献甚微。除人口统计条件化外，候选地点的构建对报告的性能影响更大。在邻近采样下，移除距离使top-1准确率提高7.7个百分点，但在流行度采样下则降低22.3个百分点，这一反转在全部三个LLM中均得到复现。这些结果区分了人口统计关联与增量预测有用性，并表明采样的下一位置准确率强烈依赖于候选替代地点的构建方式。
英文摘要
Large language models (LLMs) are increasingly used for individual next-location prediction, with personal information easily added to prompts alongside mobility history. Yet the incremental predictive value of such information remains unclear. Using linked sociodemographic records and mobility traces from 5,000 Shenzhen residents, this study separates model responsiveness from predictive value. GPT-5 is the primary model, with GPT-5.5 and Claude Opus 4.6 used for replication. In 1,000 paired prediction instances, models rank 100 candidate destinations with and without age, gender, occupation and income while all other inputs are held fixed. Behavioural history raises top-1 accuracy from 5.6% to 18.5% as prior history increases from zero to six days. By contrast, sociodemographic attributes produce no detectable overall gain, although replacing correct attributes with those of another person reduces accuracy by 5.4 percentage points. Candidate construction also matters, removing distance raises accuracy by 7.7 points under proximity sampling but lowers it by 22.3 points under popularity sampling, with the reversal reproduced across all three LLMs. These findings identify behavioural history as the clearest source of incremental value and show that personal information should be evaluated under matched, explicitly specified conditions before its privacy and governance costs are justified.
Comments20 pages, 5 figures
