将图学习视为检索生成过程，提升大模型处理图数据能力
Are Large Language Models In-Context Graph Learners?
- 把图数据查询当作检索任务，用上下文生成方式增强理解
- 在无微调条件下显著提升大模型在图任务上的表现
- 适合需快速部署、不可修改预训练模型的场景
大语言模型（LLM）在处理非结构化输入（如文本或图像）时展现出强大的上下文推理能力，但在处理图等结构化数据时因缺乏对非欧几里得结构的理解而表现不佳。未经额外微调时，其在图学习任务中的性能远低于图神经网络（GNN）。本文提出将图学习视为一种检索增强生成（RAG）过程：特定实例（如节点或边）作为查询，图本身作为检索到的上下文。基于此，我们设计了一系列RAG框架，以增强LLM在图学习任务中的上下文学习能力。全面评估表明，所提方法显著提升了LLM在图相关任务中的表现，尤其适用于无法修改预训练模型或仅通过API访问的场景。
原文摘要 · Abstract (English)
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.
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