用图结构增强大模型，提升空间推理能力
Graph-Enhanced Large Language Models for Spatial Search
- 将空间数据构建成图，让大模型通过图结构进行推理
- 解决大模型在城市规划、旅行等场景中缺乏空间理解的问题
- 适合对地理信息、智能导航感兴趣的开发者和研究者
尽管大型语言模型（LLMs）通过检索增强生成（RAG）等技术在完成复杂任务和回答领域特定问题方面取得了显著进展，但其推理能力，特别是空间推理能力仍显不足。空间推理是回答基于物理世界诸多领域问题的关键，如城市规划、土木工程和旅行等。为推动大模型在这些领域的应用，亟需开发新方法，使大模型能够基于以图形式存储的空间数据进行推理。本文探讨了利用图增强实现空间推理的挑战，并展望未来搜索系统与大模型融合，通过图增强推理回答复杂空间问题的可能性。
原文摘要 · Abstract (English)
There have been many recent improvements in the ability of Large Language Models (LLMs) to perform complex tasks and answer domain-specific questions through techniques like Retrieval Augmented Generation (RAG). However, reasoning abilities of LLMs, including spatial reasoning abilities, are still lacking. Spatial reasoning is a key component required to answer questions in a variety of domains that are grounded in the physical world, including urban planning, civil engineering, travel, and many others. To advance the development of LLMs and facilitate an impact in these domains, new research techniques must be developed to enable LLMs to reason over spatial data, which is commonly stored in the form of a graph. In this paper we outline the challenges associated with spatial reasoning through LLMs and envision a future in which search engines integrate with LLMs to answer complex spatial questions through graph-enhanced reasoning.
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