InstructGraph: Boosting Large Language Models via Graph-centric Instruction Tuning and Preference Alignment
This work addresses the challenge of improving LLMs for graph-centric tasks, which is incremental as it builds on existing instruction tuning methods with a focus on graph data.
The paper tackles the problem of enhancing large language models' performance on graph reasoning and generation tasks by proposing InstructGraph, a framework that uses instruction tuning and preference alignment, resulting in performance improvements of over 13% compared to GPT-4 and 38% compared to LLaMA2.
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.