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.

Related papers

• Are Large Language Models In-Context Graph Learners? [31.172657860606297]
  Large language models (LLMs) have remarkable in-context reasoning capabilities across a wide range of tasks. However, they struggle to handle structured data, such as graphs, due to their lack of understanding of non-Euclidean structures. We show that learning on graph data can be conceptualized as a retrieval-augmented generation (RAG) process. We propose a series of RAG frameworks to enhance the in-context learning capabilities of LLMs for graph learning tasks.
  arXiv  Detail & Related papers  (2025-02-19T09:14:19Z)
• Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks [25.720233631885726]
  integration of Graph Neural Networks (GNNs) and Large Language Models (LLMs) has emerged as a promising technological paradigm. We leverage graph description texts with rich semantic context to fundamentally enhance Data quality. This work serves as a foundational reference for researchers and practitioners looking to advance graph learning methodologies.
  arXiv  Detail & Related papers  (2024-12-17T01:41:17Z)
• A Hierarchical Language Model For Interpretable Graph Reasoning [47.460255447561906]
  We introduce Hierarchical Language Model for Graph (HLM-G), which employs a two-block architecture to capture node-centric local information and interaction-centric global structure. The proposed scheme allows LLMs to address various graph queries with high efficacy, efficiency, and robustness, while reducing computational costs on large-scale graph tasks. Comprehensive evaluations across diverse graph reasoning and real-world tasks of node, link, and graph-levels highlight the superiority of our method.
  arXiv  Detail & Related papers  (2024-10-29T00:28:02Z)
• Let's Ask GNN: Empowering Large Language Model for Graph In-Context Learning [28.660326096652437]
  We introduce AskGNN, a novel approach that bridges the gap between sequential text processing and graph-structured data. AskGNN employs a Graph Neural Network (GNN)-powered structure-enhanced retriever to select labeled nodes across graphs. Experiments across three tasks and seven LLMs demonstrate AskGNN's superior effectiveness in graph task performance.
  arXiv  Detail & Related papers  (2024-10-09T17:19:12Z)
• How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehension [53.6373473053431]
  This work introduces a benchmark to assess large language models' capabilities in graph pattern tasks. We have developed a benchmark that evaluates whether LLMs can understand graph patterns based on either terminological or topological descriptions. Our benchmark encompasses both synthetic and real datasets, and a variety of models, with a total of 11 tasks and 7 models.
  arXiv  Detail & Related papers  (2024-10-04T04:48:33Z)
• All Against Some: Efficient Integration of Large Language Models for Message Passing in Graph Neural Networks [51.19110891434727]
  Large Language Models (LLMs) with pretrained knowledge and powerful semantic comprehension abilities have recently shown a remarkable ability to benefit applications using vision and text data. E-LLaGNN is a framework with an on-demand LLM service that enriches message passing procedure of graph learning by enhancing a limited fraction of nodes from the graph.
  arXiv  Detail & Related papers  (2024-07-20T22:09:42Z)
• Multi-View Empowered Structural Graph Wordification for Language Models [12.22063024099311]
  We introduce an end-to-end modality-aligning framework for LLM-graph alignment: Dual-Residual Vector Quantized-Variational AutoEncoder, namely Dr.E. Our approach is purposefully designed to facilitate token-level alignment with LLMs, enabling an effective translation of the intrinsic'of graphs into comprehensible natural language. Our framework ensures certain visual interpretability, efficiency, and robustness, marking the promising successful endeavor to achieve token-level alignment between LLMs and GNNs.
  arXiv  Detail & Related papers  (2024-06-19T16:43:56Z)
• Parameter-Efficient Tuning Large Language Models for Graph Representation Learning [62.26278815157628]
  We introduce Graph-aware. Efficient Fine-Tuning - GPEFT, a novel approach for efficient graph representation learning. We use a graph neural network (GNN) to encode structural information from neighboring nodes into a graph prompt. We validate our approach through comprehensive experiments conducted on 8 different text-rich graphs, observing an average improvement of 2% in hit@1 and Mean Reciprocal Rank (MRR) in link prediction evaluations.
  arXiv  Detail & Related papers  (2024-04-28T18:36:59Z)
• LLaGA: Large Language and Graph Assistant [73.71990472543027]
  Large Language and Graph Assistant (LLaGA) is an innovative model to handle the complexities of graph-structured data. LLaGA excels in versatility, generalizability and interpretability, allowing it to perform consistently well across different datasets and tasks. Our experiments show that LLaGA delivers outstanding performance across four datasets and three tasks using one single model.
  arXiv  Detail & Related papers  (2024-02-13T02:03:26Z)
• Integrating Graphs with Large Language Models: Methods and Prospects [68.37584693537555]
  Large language models (LLMs) have emerged as frontrunners, showcasing unparalleled prowess in diverse applications. Merging the capabilities of LLMs with graph-structured data has been a topic of keen interest. This paper bifurcates such integrations into two predominant categories.
  arXiv  Detail & Related papers  (2023-10-09T07:59:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.