NT-LLM: A Novel Node Tokenizer for Integrating Graph Structure into Large Language Models

• URL: http://arxiv.org/abs/2410.10743v1
• Date: Mon, 14 Oct 2024 17:21:57 GMT
• Title: NT-LLM: A Novel Node Tokenizer for Integrating Graph Structure into Large Language Models
• Authors: Yanbiao Ji, Chang Liu, Xin Chen, Yue Ding, Dan Luo, Mei Li, Wenqing Lin, Hongtao Lu,
• Abstract summary: Graphs are a fundamental data structure for representing relationships in real-world scenarios. Applying Large Language Models (LLMs) to graph-related tasks poses significant challenges. We introduce Node Tokenizer for Large Language Models (NT-LLM), a novel framework that efficiently encodes graph structures.
• Score: 26.739650151993928
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graphs are a fundamental data structure for representing relationships in real-world scenarios. With the success of Large Language Models (LLMs) across various natural language processing (NLP) tasks, there has been growing interest in integrating LLMs for graph learning. However, applying LLMs to graph-related tasks poses significant challenges, as these models are not inherently designed to capture the complex structural information present in graphs. Existing approaches address this challenge through two strategies: the chain of tasks approach, which uses Graph Neural Networks (GNNs) to encode the graph structure so that LLMs are relieved from understanding spatial positions; and Graph-to-Text Conversion, which translates graph structures into semantic text representations that LLMs can process. Despite their progress, these methods often struggle to fully preserve the topological information of graphs or require extensive computational resources, limiting their practical applicability. In this work, we introduce Node Tokenizer for Large Language Models (NT-LLM), a novel framework that efficiently encodes graph structures by selecting key nodes as anchors and representing each node based on its relative distance to these anchors. This position-anchored encoding effectively captures the graph topology, enabling enhanced reasoning capabilities in LLMs over graph data. Additionally, we implement a task-specific tuning procedure to further improve structural understanding within LLMs. Through extensive empirical evaluations, NT-LLM demonstrates significant performance improvements across a variety of graph-related tasks.

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