Large language models (LLMs) have demonstrated remarkable in-context reasoning capabilities across a wide range of tasks, particularly with unstructured inputs such as language or images. However, LLMs struggle to handle structured data, such as graphs, due to their lack of understanding of non-Euclidean structures. As a result, without additional fine-tuning, their performance significantly lags behind that of graph neural networks (GNNs) in graph learning tasks. In this paper, we show that learning on graph data can be conceptualized as a retrieval-augmented generation (RAG) process, where specific instances (e.g., nodes or edges) act as queries, and the graph itself serves as the retrieved context. Building on this insight, we propose a series of RAG frameworks to enhance the in-context learning capabilities of LLMs for graph learning tasks. Comprehensive evaluations demonstrate that our proposed RAG frameworks significantly improve LLM performance on graph-based tasks, particularly in scenarios where a pretrained LLM must be used without modification or accessed via an API.

本研究解决了大型语言模型在处理图形数据方面的不足，尤其是它们在结构化数据处理中的挑战。论文提出了一种将图学习视为检索增强生成（RAG）过程的创新方法，从而提高大型语言模型在图学习任务中的上下文学习能力。研究发现，所提出的RAG框架显著提升了大型语言模型在图相关任务中的表现，尤其是在需利用预训练模型而无进一步调整的情境中。

大型语言模型是否是上下文图学习者？