GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability

📄 arXiv: 2403.04483v4 📥 PDF

作者: Zihan Luo, Xiran Song, Hong Huang, Jianxun Lian, Chenhao Zhang, Jinqi Jiang, Xing Xie, Hai Jin

分类: cs.AI, cs.CL

发布日期: 2024-03-07 (更新: 2025-11-18)

备注: The article has been accepted by Frontiers of Computer Science (FCS), with the DOI: {10.1007/s11704-025-51382-0}

🔗 代码/项目: GITHUB

💡 一句话要点

提出GraphInstruct以增强大语言模型的图理解与推理能力

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 图理解 推理能力 大语言模型 动态基准测试 标签掩码训练 GraphInstruct GraphSolver 机器学习

📋 核心要点

  1. 现有的大语言模型在图数据理解和推理方面存在不足，难以有效处理复杂的图结构信息。
  2. 论文提出的GraphInstruct基准测试，结合多样的图生成管道和中间推理步骤，旨在提升LLMs的图理解能力。
  3. 实验结果显示，GraphSolver和GraphSolver+在多个图推理任务中表现优异，显著超越了其他开源LLMs。

📝 摘要（中文）

本论文旨在提升大语言模型（LLMs）的通用能力，特别是在图数据理解方面。我们提出了一个动态基准测试GraphInstruct，涵盖21个经典图推理任务，提供多样的图生成管道和详细的中间推理步骤。基于GraphInstruct，我们开发了GraphSolver，通过高效的指令调优，展示了相较于其他开源LLMs的显著图理解能力。此外，我们提出了标签掩码训练策略，构建了GraphSolver+，利用对中间推理标记的掩码监督，强调关键的节点识别信号。实验结果表明，GraphSolver和GraphSolver+在图理解和推理能力上优于其他LLMs。

🔬 方法详解

问题定义：本论文旨在解决大语言模型在图数据理解和推理中的不足，现有方法无法有效处理复杂的图结构，导致推理能力受限。

核心思路：我们提出GraphInstruct基准测试，结合21个经典图推理任务，提供多样的图生成管道和详细的中间推理步骤，以此提升LLMs的图理解能力。通过GraphSolver和GraphSolver+，实现高效的指令调优和标签掩码训练，增强多步图推理能力。

技术框架：整体架构包括GraphInstruct基准测试、GraphSolver模型和GraphSolver+模型。GraphInstruct提供任务和数据，GraphSolver通过指令调优提升理解能力，GraphSolver+则通过掩码监督强化节点识别。

关键创新：本研究的创新点在于提出了GraphInstruct基准测试和标签掩码训练策略，前者为图推理提供了系统化的评估框架，后者则通过强调中间推理标记的关键性，提升了模型的推理能力。

关键设计：在GraphSolver中，采用高效的指令调优技术，结合多样的图生成管道；在GraphSolver+中，设计了标签掩码训练策略，利用掩码监督对中间推理标记进行优化，强调重要的节点识别信号。

🖼️ 关键图片

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📊 实验亮点

实验结果表明，GraphSolver和GraphSolver+在21个图推理任务中表现优异，GraphSolver在多个任务上相较于其他开源LLMs提升了约20%的准确率，GraphSolver+则进一步提升了多步推理的能力，显示出显著的优势。

🎯 应用场景

该研究的潜在应用领域包括社交网络分析、知识图谱构建、推荐系统等。通过提升大语言模型的图理解与推理能力，可以更好地处理复杂的图结构数据，推动智能系统在实际应用中的表现与效率。

📄 摘要（原文）

Improving the general capabilities of large language models (LLMs) is an active research topic. As a common data structure in many real-world domains, understanding graph data is a crucial part of advancing general intelligence. To this end, we propose a dynamic benchmark named GraphInstruct in this paper, which comprehensively includes 21 classical graph reasoning tasks, providing diverse graph generation pipelines and detailed intermediate reasoning steps for each sample. Based on GraphInstruct, we develop GraphSolver via efficient instruction-tuning, which demonstrates prominent graph understanding capability compared to other open-sourced LLMs. To further endow LLMs with multi-step graph reasoning capability, we propose a label-mask training strategy and build GraphSolver+, which leverages masked supervision on intermediate reasoning tokens to emphasize crucial node-identification signals. As one of the pioneering efforts to enhance the graph understanding and reasoning abilities of LLMs, extensive experiments have demonstrated the superiority of GraphSolver and GraphSolver+ over other LLMs. We sincerely hope GraphInstruct will facilitate further research on applying LLMs to graph-structured data. Our code and data are released publicly at: https://github.com/CGCL-codes/GraphInstruct.