大模型能提取全球城市知识，辅助城市研究决策。
What can LLM tell us about cities?
- 用大模型直接查询和提取隐含特征来获取城市信息
- 基于大模型特征的预测模型准确率显著提升
- 模型在陌生城市会生成通用内容，暴露知识盲区
本研究探讨了大型语言模型（LLMs）在提供全球范围城市与区域知识方面的潜力。采用两种方法：直接向大模型查询目标变量值，以及从大模型输出中提取与目标变量相关的显式和隐式特征。实验表明，大模型在全球城市中嵌入了广泛但程度不一的知识，基于大模型特征训练的机器学习模型预测准确率持续提高。此外，大模型在各大洲城市均表现出一定知识水平，但在缺乏知识时，往往生成通用或随机输出。这些发现表明，大模型为城市数据驱动决策提供了新可能。
原文摘要 · Abstract (English)
This study explores the capabilities of large language models (LLMs) in providing knowledge about cities and regions on a global scale. We employ two methods: directly querying the LLM for target variable values and extracting explicit and implicit features from the LLM correlated with the target variable. Our experiments reveal that LLMs embed a broad but varying degree of knowledge across global cities, with ML models trained on LLM-derived features consistently leading to improved predictive accuracy. Additionally, we observe that LLMs demonstrate a certain level of knowledge across global cities on all continents, but it is evident when they lack knowledge, as they tend to generate generic or random outputs for unfamiliar tasks. These findings suggest that LLMs can offer new opportunities for data-driven decision-making in the study of cities.
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