Summary of Cost-effective Label-free Node Classification with Llms, by Taiyan Zhang et al.
Cost-Effective Label-free Node Classification with LLMs
by Taiyan Zhang, Renchi Yang, Mingyu Yan, Xiaochun Ye, Dongrui Fan, Yurui Lai
First submitted to arxiv on: 16 Dec 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 The paper proposes an innovative approach to address the issue of inadequate labeled data in graph neural networks (GNNs) for node classification tasks. It leverages large language models (LLMs) to generate high-quality labels, capitalizing on their zero-shot capabilities and vast knowledge base. The methodology is designed to reduce the reliance on expensive human-labeled data, making GNN training more practical and efficient. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to classify objects in a picture using AI. Graph neural networks are great for this task, but they need labeled examples to learn. But labeling these examples can be very time-consuming and costly. The researchers found that large language models can actually help with this problem by generating labels automatically. This approach has the potential to make graph neural networks more practical and efficient. |
Keywords
» Artificial intelligence » Classification » Gnn » Knowledge base » Zero shot
