Summary of Llms As Zero-shot Graph Learners: Alignment Of Gnn Representations with Llm Token Embeddings, by Duo Wang and Yuan Zuo and Fengzhi Li and Junjie Wu
LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings
by Duo Wang, Yuan Zuo, Fengzhi Li, Junjie Wu
First submitted to arxiv on: 25 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel framework called Token Embedding-Aligned Graph Language Model (TEA-GLM) that leverages large language models (LLMs) for zero-shot graph machine learning. Inspired by the zero-shot capabilities of instruction-fine-tuned LLMs, TEA-GLM pretrains a graph neural network (GNN), aligning its representations with token embeddings of an LLM. The framework then trains a linear projector that transforms the GNN’s representations into graph token embeddings without tuning the LLM. A unified instruction is designed for various graph tasks at different levels, such as node classification and link prediction. Experiments show that TEA-GLM achieves state-of-the-art performance on unseen datasets and tasks compared to other methods using LLMs as predictors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores a new way to use big language models for learning from graph data without needing labeled examples. The idea is to align the representations of graph neural networks with those of language models, allowing the language model to predict results on unseen graphs. This approach helps the model generalize better and perform well even when it’s never seen similar data before. The authors test their method on various graph tasks and show that it outperforms other methods in zero-shot learning settings. |
Keywords
» Artificial intelligence » Classification » Embedding » Gnn » Graph neural network » Language model » Machine learning » Token » Zero shot
