InstructGraph: Boosting Large Language Models via Graph-centric
  Instruction Tuning and Preference Alignment
Jianing Wang Junda Wu Yupeng Hou Yao Liu Ming Gao Julian McAuley
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.