Summary of Login: a Large Language Model Consulted Graph Neural Network Training Framework, by Yiran Qiao et al.
LOGIN: A Large Language Model Consulted Graph Neural Network Training Framework
by Yiran Qiao, Xiang Ao, Yang Liu, Jiarong Xu, Xiaoqian Sun, Qing He
First submitted to arxiv on: 22 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Recent advances in graph machine learning have focused on designing advanced variants of Graph Neural Networks (GNNs) to maintain superior performance on different graphs. This paper proposes a novel paradigm, “LLMs-as-Consultants,” which integrates Large Language Models (LLMs) with GNNs in an interactive manner. The framework, named LOGIN, empowers the utilization of LLMs within the GNN training process. The authors formulate concise prompts for spotted nodes, carrying semantic and topological information, serving as input to LLMs. They refine GNNs by utilizing responses from LLMs, depending on their correctness. Empirical evaluation on node classification tasks across homophilic and heterophilic graphs demonstrates that basic GNN architectures can achieve comparable performance to advanced GNNs with intricate designs. This work leverages the strengths of LLMs and GNNs to streamline the design process and improve performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using a new way to train Graph Neural Networks (GNNs) that combines them with Large Language Models (LLMs). The idea is called “LLMs-as-Consultants.” It helps GNNs get better results by getting hints from LLMs. The researchers came up with special prompts for certain nodes, giving the LLMs information about the graph and what it looks like. They then use the LLMs’ answers to fine-tune the GNNs. This new approach works well even when using simple GNN designs. The paper shows that this method can help improve performance on different types of graphs. |
Keywords
» Artificial intelligence » Classification » Gnn » Machine learning
