Summary of Gl-fusion: Rethinking the Combination Of Graph Neural Network and Large Language Model, by Haotong Yang et al.
GL-Fusion: Rethinking the Combination of Graph Neural Network and Large Language model
by Haotong Yang, Xiyuan Wang, Qian Tao, Shuxian Hu, Zhouchen Lin, Muhan Zhang
First submitted to arxiv on: 8 Dec 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 proposed architecture, GL-Fusion, addresses the limitations of integrating Large Language Models (LLMs) with Graph Neural Networks (GNNs) by deeply integrating GNN with LLM. This is achieved through three key innovations: Structure-Aware Transformers, Graph-Text Cross-Attention, and GNN-LLM Twin Predictor. The model enables simultaneous processing of textual and structural information, generates outputs from both GNN and LLM, and achieves state-of-the-art performance on OGBN-Arxiv and OGBG-Code2. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to combine Large Language Models (LLMs) with Graph Neural Networks (GNNs). This helps solve problems like capturing graph structures or understanding complex text. The new architecture, called GL-Fusion, does this by letting the models work together more closely. It uses special layers and attention mechanisms to process both text and graph data at the same time. This allows it to generate outputs from both GNN and LLM, making it useful for a range of tasks. |
Keywords
» Artificial intelligence » Attention » Cross attention » Gnn
