GraphLLM：提升大语言模型的图推理能力
GraphLLM: Boosting Graph Reasoning Ability of Large Language Model
AI总结：
针对大语言模型图推理能力不足、Graph2Text转换存在瓶颈的问题，提出将图学习模型与大语言模型协同整合的GraphLLM方法，大幅提升了图推理准确率并减少了上下文长度。
摘要：
大语言模型（LLMs）的发展显著拓展了通用人工智能（AGI）的边界，它们在理解包括图像、音频在内的各类信息方面具备出色能力。尽管取得了这些进展，在让LLMs熟练理解和推理图数据方面仍存在关键缺口。近期研究凸显了LLMs在基础图推理任务上表现不佳的问题。本文致力于发掘阻碍LLMs开展图推理的因素，指出将图转换为自然语言描述（Graph2Text）的常规做法是根本瓶颈。为克服这一障碍，我们提出了GraphLLM，这是一种开创性的端到端方法，将图学习模型与LLMs协同整合。这种协同效应借助图学习模型的超强表达能力，让LLMs具备熟练解读和推理图数据的能力。我们在四项基础图推理任务上开展的实证评估验证了GraphLLM的有效性。结果显示，在各类图推理任务中，其平均准确率大幅提升了54.44%，同时上下文长度显著减少了96.45%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
