用图神经网络增强大模型的上下文学习，让LLM更懂图数据。
Let's Ask GNN: Empowering Large Language Model for Graph In-Context Learning
- 用GNN构建检索器，从图中选出最有帮助的标注节点。
- 在7个大模型上测试，跨3类任务性能显著提升。
- 无需微调，适合想快速用LLM处理图数据的研究者。
文本属性图（TAGs）对建模复杂现实系统至关重要，但大语言模型（LLMs）在处理这类图结构数据时面临序列化文本与图结构之间的鸿沟。我们提出AskGNN，一种通过上下文学习（ICL）将图数据和任务信息融入LLMs的新方法。AskGNN采用基于图神经网络（GNN）的结构增强型检索器，从图中选择包含复杂结构与监督信号的标注节点。我们的学习-检索算法优化检索器，使其选出能最大化LLM在图任务上表现的示例节点。在三个任务和七个大模型上的实验表明，AskGNN在图任务性能上显著优于基线方法，为无需大量微调即可应用LLMs于图结构数据开辟了新路径。
原文摘要 · Abstract (English)
Textual Attributed Graphs (TAGs) are crucial for modeling complex real-world systems, yet leveraging large language models (LLMs) for TAGs presents unique challenges due to the gap between sequential text processing and graph-structured data. We introduce AskGNN, a novel approach that bridges this gap by leveraging In-Context Learning (ICL) to integrate graph data and task-specific information into LLMs. AskGNN employs a Graph Neural Network (GNN)-powered structure-enhanced retriever to select labeled nodes across graphs, incorporating complex graph structures and their supervision signals. Our learning-to-retrieve algorithm optimizes the retriever to select example nodes that maximize LLM performance on graph. Experiments across three tasks and seven LLMs demonstrate AskGNN's superior effectiveness in graph task performance, opening new avenues for applying LLMs to graph-structured data without extensive fine-tuning.
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