让大模型精准回答地理路线上的餐厅、景点等空间问题。
Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questions
- 用混合检索器融合地理筛选与语义匹配，提升空间查询精度。
- 在多个地图问答数据集上，准确率和排序性能显著优于基线。
- 适合需要结合地理位置与自然语言理解的智能导航、旅游应用。
回答现实世界中的地理空间问题——如在旅行路线上找餐厅或地标附近的设施——需要同时处理地理关系与用户语义意图。然而，现有大语言模型缺乏空间计算能力且无法访问最新、广泛的真实地理数据；传统地理信息系统则难以理解自然语言。为此，我们提出 Spatial-RAG，一种专为地理空间问答设计的检索增强生成框架。该框架通过混合空间检索器，将结构化地理数据库与大语言模型结合，整合稀疏空间过滤与密集语义匹配。其回答过程被建模为对空间与语义相关性的多目标优化，识别帕累托最优候选，并根据用户意图动态选择最佳响应。在多个旅游与地图问答数据集上的实验表明，Spatial-RAG 在准确率、精确率与排名性能上均显著优于强基线模型。
原文摘要 · Abstract (English)
Answering real-world geospatial questions--such as finding restaurants along a travel route or amenities near a landmark--requires reasoning over both geographic relationships and semantic user intent. However, existing large language models (LLMs) lack spatial computing capabilities and access to up-to-date, ubiquitous real-world geospatial data, while traditional geospatial systems fall short in interpreting natural language. To bridge this gap, we introduce Spatial-RAG, a Retrieval-Augmented Generation (RAG) framework designed for geospatial question answering. Spatial-RAG integrates structured spatial databases with LLMs via a hybrid spatial retriever that combines sparse spatial filtering and dense semantic matching. It formulates the answering process as a multi-objective optimization over spatial and semantic relevance, identifying Pareto-optimal candidates and dynamically selecting the best response based on user intent. Experiments across multiple tourism and map-based QA datasets show that Spatial-RAG significantly improves accuracy, precision, and ranking performance over strong baselines.
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