Do current large language models (LLMs) better solve graph reasoning and
generation tasks with parameter updates? In this paper, we propose
InstructGraph, a framework that empowers LLMs with the abilities of graph
reasoning and generation by instruction tuning and preference alignment.
Specifically, we first propose a structured format verbalizer to unify all
graph data into a universal code-like format, which can simply represent the
graph without any external graph-specific encoders. Furthermore, a graph
instruction tuning stage is introduced to guide LLMs in solving graph reasoning
and generation tasks. Finally, we identify potential hallucination problems in
graph tasks and sample negative instances for preference alignment, the target
of which is to enhance the output's reliability of the model. Extensive
experiments across multiple graph-centric tasks exhibit that InstructGraph can
achieve the best performance and outperform GPT-4 and LLaMA2 by more than 13\%
and 38\%, respectively.

通过指令调整和偏好对齐，InstructGraph 框架赋予大型语言模型（LLMs）图推理和生成能力。该框架中，我们首先提出了结构化格式化语言器来统一所有图数据，并且引入了图指令调整阶段来指导 LLMs 解决图推理和生成任务。最后，我们针对图任务中的潜在幻觉问题抽取了负例样本以进行偏好对齐，以提高模型输出的可靠性。通过多个图相关任务的广泛实验，InstructGraph 实现了最佳性能，并且超过 GPT-4 和 LLaMA2 分别 13% 和 38% 以上。