Text-Attributed Graphs (TAGs) are graphs of connected textual documents. Graph models can efficiently learn TAGs, but their training heavily relies on human-annotated labels, which are scarce or even unavailable in many applications. Large language models (LLMs) have recently demonstrated remarkable capabilities in few-shot and zero-shot TAG learning, but they suffer from scalability, cost, and privacy issues. Therefore, in this work, we focus on synergizing LLMs and graph models with their complementary strengths by distilling the power of LLMs to a local graph model on TAG learning. To address the inherent gaps between LLMs (generative models for texts) and graph models (discriminative models for graphs), we propose first to let LLMs teach an interpreter with rich textual rationale and then let a student model mimic the interpreter's reasoning without LLMs' textual rationale. Extensive experiments validate the efficacy of our proposed framework.

本研究提出了使用大型语言模型和图模型相结合的方法来学习文本属性图 (TAG)，通过将大型语言模型的能力提取到本地图模型上，以解决大型语言模型的可扩展性、成本和隐私问题，并通过丰富的文本理由教授解释器，然后让学生模型模仿解释器的推理来弥补大型语言模型和图模型之间的固有差距。通过广泛实验证明了该方法的有效性。

大规模语言模型蒸馏用于文本属性图学习