Evaluating and enhancing the general capabilities of large language models
(LLMs) has been an important research topic. Graph is a common data structure
in the real world, and understanding graph data is a crucial part for advancing
general intelligence. To evaluate and enhance the graph understanding abilities
of LLMs, in this paper, we propose a benchmark named GraphInstruct, which
comprehensively includes 21 classical graph reasoning tasks, providing diverse
graph generation pipelines and detailed reasoning steps. Based on
GraphInstruct, we further construct GraphLM through efficient
instruction-tuning, which shows prominent graph understanding capability. In
order to enhance the LLM with graph reasoning capability as well, we propose a
step mask training strategy, and construct a model named GraphLM+. As one of
the pioneering efforts to enhance the graph understanding and reasoning
abilities of LLMs, extensive experiments have demonstrated the superiority of
GraphLM and GraphLM+ over other LLMs. We look forward to more researchers
exploring the potential of LLMs in the graph data mining domain through
GraphInstruct. Our code for generating GraphInstruct is released publicly at:
this https URL

通过提出一个名为 GraphInstruct 的基准测试以及 GraphLM 和 GraphLM + 模型的构建和实验，本文旨在评估和增强大型语言模型在图形理解和推理方面的能力。

GraphInstruct: 强化大型语言模型的图理解和推理能力

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

Large language models (LLMs) have achieved impressive success across several
fields, but their proficiency in understanding and resolving complex graph
problems is less explored. To bridge this gap, we introduce GraphInstruct, a
novel and comprehensive instruction-tuning dataset designed to equip language
models with the ability to tackle a broad spectrum of graph problems using
explicit reasoning paths. Utilizing GraphInstruct, we build GraphWiz, an
open-source language model capable of resolving various graph problem types
while generating clear reasoning processes. To enhance the model's capability
and reliability, we incorporate the Direct Preference Optimization (DPO)
framework into the graph problem-solving context. The enhanced model,
GraphWiz-DPO, achieves an average accuracy of 65% across nine tasks with
different complexity levels, surpassing GPT-4 which has an average accuracy of
43.8%. Moreover, our research delves into the delicate balance between training
data volume and model performance, highlighting the potential for overfitting
with increased data. We also explore the transferability of the model's
reasoning ability across different graph tasks, indicating the model's
adaptability and practical application potential. Our investigation offers a
new blueprint and valuable insights for developing LLMs specialized in graph
reasoning and problem-solving.

通过引入 GraphInstruct 数据集和 GraphWiz-DPO 模型，本研究在复杂图问题领域取得突破，展示了策略性推理路径训练方法以及模型的实用应用潜力。