GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability

• URL: http://arxiv.org/abs/2403.04483v3
• Date: Mon, 27 Oct 2025 08:07:17 GMT
• Title: GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability
• Authors: Zihan Luo, Xiran Song, Hong Huang, Jianxun Lian, Chenhao Zhang, Jinqi Jiang, Xing Xie, Hai Jin,
• Abstract summary: We propose a benchmark named GraphInstruct, which comprehensively includes 21 classical graph reasoning tasks.<n>Based on GraphInstruct, we develop Graphr via efficient instruction-tuning, which demonstrates prominent graph understanding capability.<n>To further endow LLMs with multi-step graph reasoning capability, we propose a label-mask training strategy and build Graphr+.
• Score: 47.69392503009922
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Improving the general capabilities of large language models (LLMs) is an active research topic. As a common data structure in many real-world domains, understanding graph data is a crucial part of advancing general intelligence. To this end, we propose a dynamic benchmark named GraphInstruct in this paper, which comprehensively includes 21 classical graph reasoning tasks, providing diverse graph generation pipelines and detailed intermediate reasoning steps for each sample. Based on GraphInstruct, we develop GraphSolver via efficient instruction-tuning, which demonstrates prominent graph understanding capability compared to other open-sourced LLMs. To further endow LLMs with multi-step graph reasoning capability, we propose a label-mask training strategy and build GraphSolver+, which leverages masked supervision on intermediate reasoning tokens to emphasize crucial node-identification signals. As one of the pioneering efforts to enhance the graph understanding and reasoning abilities of LLMs, extensive experiments have demonstrated the superiority of GraphSolver and GraphSolver+ over other LLMs. We sincerely hope GraphInstruct will facilitate further research on applying LLMs to graph-structured data. Our code and data are released publicly at: https://github.com/CGCL-codes/GraphInstruct.

Related papers

• 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)
• What Do LLMs Need to Understand Graphs: A Survey of Parametric Representation of Graphs [69.48708136448694]
  Large language models (LLMs) are reorganizing in the AI community for their expected reasoning and inference abilities. We believe this kind of parametric representation of graphs, graph laws, can be a solution for making LLMs understand graph data as the input.
  arXiv  Detail & Related papers  (2024-10-16T00:01:31Z)
• Can Large Language Models Analyze Graphs like Professionals? A Benchmark, Datasets and Models [90.98855064914379]
  We introduce ProGraph, a benchmark for large language models (LLMs) to process graphs. Our findings reveal that the performance of current LLMs is unsatisfactory, with the best model achieving only 36% accuracy. We propose LLM4Graph datasets, which include crawled documents and auto-generated codes based on 6 widely used graph libraries.
  arXiv  Detail & Related papers  (2024-09-29T11:38:45Z)
• GraphInsight: Unlocking Insights in Large Language Models for Graph Structure Understanding [17.724492441325165]
  Large Language Models (LLMs) struggle with comprehending graphical structure information through prompts of graph description sequences. We propose GraphInsight, a novel framework aimed at improving LLMs' comprehension of both macro- and micro-level graphical information.
  arXiv  Detail & Related papers  (2024-09-05T05:34:16Z)
• Joint Embeddings for Graph Instruction Tuning [0.0]
  This work explores the integration of the graph modality in Large Language Models (LLMs) for general graph instruction following tasks. It aims at producing a deep learning model that enhances an underlying LLM with graph embeddings and trains it to understand them. The approach performs significantly better than a graph to text approach and remains consistent even for larger graphs.
  arXiv  Detail & Related papers  (2024-05-31T08:26:47Z)
• Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs [60.71360240206726]
  Large language models (LLMs) suffer from hallucinations, especially on knowledge-intensive tasks. Existing works propose to augment LLMs with individual text units retrieved from external knowledge corpora. We propose a framework called Graph Chain-of-thought (Graph-CoT) to augment LLMs with graphs by encouraging LLMs to reason on the graph iteratively.
  arXiv  Detail & Related papers  (2024-04-10T15:41:53Z)
• Exploring the Potential of Large Language Models in Graph Generation [51.046188600990014]
  Graph generation requires large language models (LLMs) to generate graphs with given properties. This paper explores the abilities of LLMs for graph generation with systematical task designs and experiments. Our evaluations demonstrate that LLMs, particularly GPT-4, exhibit preliminary abilities in graph generation tasks.
  arXiv  Detail & Related papers  (2024-03-21T12:37:54Z)
• 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)
• GraphGPT: Graph Instruction Tuning for Large Language Models [27.036935149004726]
  Graph Neural Networks (GNNs) have evolved to understand graph structures. To enhance robustness, self-supervised learning (SSL) has become a vital tool for data augmentation. Our research tackles this by advancing graph model generalization in zero-shot learning environments.
  arXiv  Detail & Related papers  (2023-10-19T06:17:46Z)
• 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)
• Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT [10.879701971582502]
  We aim to develop a large language model (LLM) with the reasoning ability on complex graph data. Inspired by the latest ChatGPT and Toolformer models, we propose the Graph-ToolFormer framework to teach LLMs themselves with prompts augmented by ChatGPT to use external graph reasoning API tools.
  arXiv  Detail & Related papers  (2023-04-10T05:25:54Z)

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.