用空间增强的检索生成技术，让大模型更准地推荐步行路线。
Spatially-Enhanced Retrieval-Augmented Generation for Walkability and Urban Discovery
- 结合空间检索与大模型生成，支持交互式步行路线推荐
- 能根据用户空间约束和偏好动态获取路径信息
- 适合城市探索、旅游规划等需要地理理解的场景
大型语言模型（LLMs）已成为人工智能的基础工具，广泛应用于城市系统与旅游推荐等非传统自然语言处理任务。然而，其幻觉问题及在空间检索与推理方面的局限性已广为人知，亟需新解决方案。检索增强生成（RAG）近年来被证明是提升LLMs准确性、领域相关性和时效性的有效方法。空间RAG进一步将该范式拓展至地理理解任务。本文提出WalkRAG，一种基于空间RAG的框架，具备对话界面，用于推荐可步行的城市行程。用户可提出满足特定空间约束与偏好的路线请求，并在过程中交互式检索路径及沿途兴趣点（POIs）信息。初步结果表明，结合信息检索、空间推理与大模型，能有效支持城市发现。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have become foundational tools in artificial intelligence, supporting a wide range of applications beyond traditional natural language processing, including urban systems and tourist recommendations. However, their tendency to hallucinate and their limitations in spatial retrieval and reasoning are well known, pointing to the need for novel solutions. Retrieval-augmented generation (RAG) has recently emerged as a promising way to enhance LLMs with accurate, domain-specific, and timely information. Spatial RAG extends this approach to tasks involving geographic understanding. In this work, we introduce WalkRAG, a spatial RAG-based framework with a conversational interface for recommending walkable urban itineraries. Users can request routes that meet specific spatial constraints and preferences while interactively retrieving information about the path and points of interest (POIs) along the way. Preliminary results show the effectiveness of combining information retrieval, spatial reasoning, and LLMs to support urban discovery.
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