LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings

• URL: http://arxiv.org/abs/2408.14512v1
• Date: Sun, 25 Aug 2024 04:32:45 GMT
• Title: LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings
• Authors: Duo Wang, Yuan Zuo, Fengzhi Li, Junjie Wu,
• Abstract summary: We introduce a novel framework named Token Embedding-Aligned Graph Language Model (TEA-GLM) We pretrain a GNN, aligning its representations with token embeddings of an LLM. We then train a linear projector that transforms the GNN's representations into a fixed number of graph token embeddings.
• Score: 7.302176015732192
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Zero-shot graph machine learning, especially with graph neural networks (GNNs), has garnered significant interest due to the challenge of scarce labeled data. While methods like self-supervised learning and graph prompt learning have been extensively explored, they often rely on fine-tuning with task-specific labels, limiting their effectiveness in zero-shot scenarios. Inspired by the zero-shot capabilities of instruction-fine-tuned large language models (LLMs), we introduce a novel framework named Token Embedding-Aligned Graph Language Model (TEA-GLM) that leverages LLMs as cross-dataset and cross-task zero-shot learners for graph machine learning. Concretely, we pretrain a GNN, aligning its representations with token embeddings of an LLM. We then train a linear projector that transforms the GNN's representations into a fixed number of graph token embeddings without tuning the LLM. A unified instruction is designed for various graph tasks at different levels, such as node classification (node-level) and link prediction (edge-level). These design choices collectively enhance our method's effectiveness in zero-shot learning, setting it apart from existing methods. Experiments show that our graph token embeddings help the LLM predictor achieve state-of-the-art performance on unseen datasets and tasks compared to other methods using LLMs as predictors.

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