用可学习的图池化标记提升大模型对图数据的表示能力。
Query-Aware Learnable Graph Pooling Tokens as Prompt for Large Language Models
- 引入可学习的图池化令牌，作为大模型的输入提示。
- 在GraphQA上提升4.13%性能，无需微调大模型。
- 适合处理带文本属性的复杂图数据任务。
图结构数据在社交网络、引用网络、常识推理图和知识图谱等领域至关重要。尽管图神经网络已被广泛用于图处理，但近期研究探索了将大语言模型应用于图任务。本文提出一种新方法——可学习图池化令牌（LGPT），解决了节点级投影的可扩展性问题和图级投影的信息损失问题。LGPT通过引入可学习参数作为大语言模型中的令牌，实现灵活高效的图表示，兼顾细粒度与全局信息。此外，我们还提出早期查询融合技术，在构建图表示前融合查询上下文，生成更有效的图嵌入。该方法在GraphQA基准上实现了4.13%的性能提升，且无需训练大语言模型，显著提升了对复杂文本属性图数据的处理能力。
原文摘要 · Abstract (English)
Graph-structured data plays a vital role in numerous domains, such as social networks, citation networks, commonsense reasoning graphs and knowledge graphs. While graph neural networks have been employed for graph processing, recent advancements have explored integrating large language models for graph-based tasks. In this paper, we propose a novel approach named Learnable Graph Pooling Token (LGPT), which addresses the limitations of the scalability issues in node-level projection and information loss in graph-level projection. LGPT enables flexible and efficient graph representation by introducing learnable parameters that act as tokens in large language models, balancing fine-grained and global graph information. Additionally, we investigate an Early Query Fusion technique, which fuses query context before constructing the graph representation, leading to more effective graph embeddings. Our method achieves a 4.13\% performance improvement on the GraphQA benchmark without training the large language model, demonstrating significant gains in handling complex textual-attributed graph data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。