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GraphLLM: Boosting Graph Reasoning Ability of Large Language Model

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arxiv 2310.05845 v1 pith:3WDXGO4J submitted 2023-10-09 cs.CL cs.AI

Ziwei Chai , Tianjie Zhang , Liang Wu , Kaiqiao Han , Xiaohai Hu , Xuanwen Huang , Yang Yang This is my paper · ORCID
classification cs.CLcs.AI
keywords graphllmsreasoningabilityfundamentalgraphllmlanguagemodels
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 9 Pith papers

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