让大模型从地图中还原行车轨迹，展现地理推理能力。
Understanding the Geospatial Reasoning Capabilities of LLMs: A Trajectory Recovery Perspective
- 用道路网络作上下文，让大模型在无导航工具下重建被遮蔽的轨迹。
- 在4000条真实轨迹上表现优于现有模型，零样本泛化能力强。
- 揭示了模型对区域和交通方式的系统性偏差，可融合用户偏好优化导航。
我们研究大型语言模型（LLMs）的地理空间推理能力，重点是其能否读取道路网络地图并完成导航。将轨迹恢复作为代理任务，要求模型重构被遮蔽的GPS轨迹，并构建了涵盖4000多条真实轨迹的GLOBALTRACE数据集，覆盖多样地区与交通方式。通过以道路网络为上下文的提示框架，模型无需外部导航工具即可生成有效路径。实验表明，LLMs在轨迹恢复任务上超越现有基线模型与专用轨迹恢复模型，具备强零样本泛化能力。细粒度分析显示，模型对道路网络和坐标系统有较强理解，但存在对地区与交通方式的系统性偏差。最后，我们展示了如何通过灵活的地图推理提升导航体验，融入用户偏好。
原文摘要 · Abstract (English)
We explore the geospatial reasoning capabilities of Large Language Models (LLMs), specifically, whether LLMs can read road network maps and perform navigation. We frame trajectory recovery as a proxy task, which requires models to reconstruct masked GPS traces, and introduce GLOBALTRACE, a dataset with over 4,000 real-world trajectories across diverse regions and transportation modes. Using road network as context, our prompting framework enables LLMs to generate valid paths without accessing any external navigation tools. Experiments show that LLMs outperform off-the-shelf baselines and specialized trajectory recovery models, with strong zero-shot generalization. Fine-grained analysis shows that LLMs have strong comprehension of the road network and coordinate systems, but also pose systematic biases with respect to regions and transportation modes. Finally, we demonstrate how LLMs can enhance navigation experiences by reasoning over maps in flexible ways to incorporate user preferences.
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