The advancement of Large Language Models (LLMs) has remarkably pushed the boundaries towards artificial general intelligence (AGI), with their exceptional ability on understanding diverse types of information, including but not limited to images and audio. Despite this progress, a critical gap remains in empowering LLMs to proficiently understand and reason on graph data. Recent studies underscore LLMs' underwhelming performance on fundamental graph reasoning tasks. In this paper, we endeavor to unearth the obstacles that impede LLMs in graph reasoning, pinpointing the common practice of converting graphs into natural language descriptions (Graph2Text) as a fundamental bottleneck. To overcome this impediment, we introduce GraphLLM, a pioneering end-to-end approach that synergistically integrates graph learning models with LLMs. This synergy equips LLMs with the ability to proficiently interpret and reason on graph data, harnessing the superior expressive power of graph learning models. Our empirical evaluations across four fundamental graph reasoning tasks validate the effectiveness of GraphLLM. The results exhibit a substantial average accuracy enhancement of 54.44%, alongside a noteworthy context reduction of 96.45% across various graph reasoning tasks.

翻译：大型语言模型（LLMs）的进步显著推动了人工通用智能（AGI）的发展，使其具备理解包括图像和音频在内的多种信息的卓越能力。然而，在赋予LLMs对图数据的熟练理解与推理能力方面仍存在关键空白。近期研究凸显了LLMs在基本图推理任务上的欠佳表现。本文致力于揭示阻碍LLMs进行图推理的障碍，指出将图转换为自然语言描述（Graph2Text）的常见做法是根本瓶颈。为克服这一障碍，我们提出GraphLLM，一种开创性的端到端方法，将图学习模型与LLMs协同集成。这种协同使LLMs具备熟练解读与推理图数据的能力，充分利用图学习模型优越的表达能力。我们在四项基本图推理任务上的实证评估验证了GraphLLM的有效性。结果显示，在各种图推理任务中，平均准确率显著提升54.44%，同时上下文减少96.45%。