AI 中文总结
研究大语言模型在城市信息层面的作用，通过审核其餐厅推荐，发现存在编造与忽略场所现象，且在不同人群中有选择性差异，模拟显示会影响消费需求，揭示其对城市信息分配不均及经济、不平等有潜在影响。
AI 中文摘要
大语言模型正成为城市之上的新信息层，塑造着人们发现、考虑并最终前往的地方。但对于它们凸显哪些地方、忽略哪些地方，以及这些模式是否因社区和用户而异并转化为现实经济后果，人们知之甚少。本文使用涵盖收入、年龄、性别和居住状况的320个合成用户档案，对美国五个城市304个社区中三个主要大语言模型的餐厅推荐进行审核。发现大语言模型既会编造场所，也会系统性地忽略真实场所。编造集中在数字和实体足迹较弱的社区，提供经过验证的场所列表时会消失。而不可见性持续存在，即使从固定的真实场所集合中选择，47.5%的场所从未被推荐，31.9%的盲点在所有三个模型系列中都存在。这种选择性也适用于用户，高收入用户会收到更昂贵、不太受欢迎的场所，游客会被导向比当地居民更昂贵但社交更多样化的场所。模拟消费者需求的变化表明，广泛依赖大语言模型推荐会使客流量和收入从连锁和快餐餐厅转向独立和全服务餐厅。研究结果表明，大语言模型充当城市信息的选择性层，在场所和人群中不均衡地分配可见性，对当地经济和城市不平等有潜在影响。
英文摘要
Large language models (LLMs) are emerging as a new informational layer over cities, shaping which places people discover, consider, and ultimately visit. Yet little is known about which places they surface, which they ignore, and whether these patterns vary across communities and users and translate into real-world economic consequences. Here, we audit restaurant recommendations from three major LLMs across 304 neighborhoods in five U.S. cities using 320 synthetic user profiles spanning income, age, sex, and residential status. We find that LLMs both fabricate venues and systematically overlook real ones. Fabrication is concentrated in neighborhoods with weaker digital and physical footprints and disappears when models are provided with verified venue lists. In contrast, invisibility persists: even when choosing from a fixed set of real venues, 47.5% of establishments are never recommended, and 31.9% of these blind spots are shared across all three model families, indicating that uneven visibility reflects not only missing knowledge but also stable patterns of selective attention rooted in shared patterns of visibility rather than model-specific errors. The same selectivity extends to users. Within identical venue pools, higher-income users receive more expensive and less popular venues, while tourists are directed toward costlier but more socially diverse establishments than local residents. Simulating the resulting shifts in consumer demand suggests that widespread reliance on LLM recommendations would redirect visits and revenue away from chain and quick-service restaurants toward independent and full-service dining. Together, our findings show that LLMs act as a selective layer of urban information that unevenly distributes visibility across places and people, with potential consequences for local economies and urban inequality.
