让大模型在知识图谱上分社区思考，更快更准地推理
Fast Think-on-Graph: Wider, Deeper and Faster Reasoning of Large Language Model on Knowledge Graph
- 分社区推理：用社区检测挖掘深层关联
- 双阶段剪枝加速检索，密度高的图也不卡顿
- 把图结构转成文本，大模型理解更自然
图检索增强生成（GRAG）通过将知识图谱（KG）信息融入大语言模型（LLMs），缓解幻觉问题。但现有方法存在局限：简单范式难以应对复杂问题，因仅捕捉浅层关联；强耦合方法在密集图上计算成本高、耗时长。本文提出快速图上思考（FastToG）新范式，使LLM在KG中按社区分步推理。FastToG采用社区检测实现深层关联挖掘，并引入粗粒度与细粒度双阶段剪枝以加速检索。此外，设计两种社区到文本转换方法，将图结构转化为自然文本，提升LLM的理解能力。实验表明，FastToG在准确率、推理速度和可解释性上均优于先前方法。
原文摘要 · Abstract (English)
Graph Retrieval Augmented Generation (GRAG) is a novel paradigm that takes the naive RAG system a step further by integrating graph information, such as knowledge graph (KGs), into large-scale language models (LLMs) to mitigate hallucination. However, existing GRAG still encounter limitations: 1) simple paradigms usually fail with the complex problems due to the narrow and shallow correlations capture from KGs 2) methods of strong coupling with KGs tend to be high computation cost and time consuming if the graph is dense. In this paper, we propose the Fast Think-on-Graph (FastToG), an innovative paradigm for enabling LLMs to think ``community by community" within KGs. To do this, FastToG employs community detection for deeper correlation capture and two stages community pruning - coarse and fine pruning for faster retrieval. Furthermore, we also develop two Community-to-Text methods to convert the graph structure of communities into textual form for better understanding by LLMs. Experimental results demonstrate the effectiveness of FastToG, showcasing higher accuracy, faster reasoning, and better explainability compared to the previous works.
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