用图结构提升大模型的准确性、推理力和结构化数据理解能力
Position: How can Graphs Help Large Language Models?
- 用图作为实时知识源，减少大模型幻觉
- 图提示技术显著增强大模型逻辑推理能力
- 让大模型更好处理电商、代码等结构化数据
随着大语言模型（LLMs）的快速发展，经典图学习任务受益于其文本特征编码优化、文本构图效率提升以及知识图谱推理增强。本文提出反向问题：图如何帮助大语言模型？从三方面展开：1）图提供最新知识源，降低大模型幻觉；2）基于图的提示方法（如思维链CoT、树状思维ToT、图状思维GoT）增强推理能力；3）将图融入大模型可提升其对结构化数据的理解，拓展在电商、代码、关系数据库（RDBs）等领域的应用。展望未来方向包括基于图设计稀疏大模型架构及类脑记忆系统。
原文摘要 · Abstract (English)
With the rapid advancement of large language models (LLMs), classic graph learning tasks have greatly benefited from LLMs, including improved encoding of textual features, more efficient construction of graphs from text, and enhanced reasoning over knowledge graphs. In this paper, we ask a complementary question: How can graphs help LLMs? We address this question from three perspectives: 1) graphs provide an up-to-date knowledge source that helps reduce LLM hallucinations, 2) graph-based prompting techniques-such as Chain-of-Thought (CoT), Tree-of-Thought (ToT), and Graph-of-Thought (GoT)-enhance LLM reasoning capabilities, and 3) integrating graphs into LLMs improves their understanding of structured data, expanding their applicability to domains such as e-commerce, code, and relational databases (RDBs). We further outlook some future directions including designing sparse LLM architectures based on graphs and brain-inspired memory systems.
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