Summary of Large Language Model Meets Graph Neural Network in Knowledge Distillation, by Shengxiang Hu et al.
Large Language Model Meets Graph Neural Network in Knowledge Distillation
by Shengxiang Hu, Guobing Zou, Song Yang, Yanglan Gan, Bofeng Zhang, Yixin Chen
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 This paper proposes a novel approach called TOGCL (Target-Prompt Online Graph Collaborative Learning) to accurately predict Quality of Service (QoS) in service-oriented architectures. The authors highlight the limitations of existing methods, which fail to capture high-order latent collaborative relationships between users and services, and do not dynamically adjust feature learning for each user-service invocation. They argue that this is crucial for learning accurate features. TOGCL leverages a dynamic graph representation of user-service interactions to model historical relationships and develop a target-prompt graph attention network to extract deep latent features of users and services. Additionally, it uses a multi-layer Transformer encoder to uncover temporal feature evolution patterns, leading to precise QoS prediction. The authors demonstrate the effectiveness of TOGCL through experiments on the WS-DREAM dataset, achieving improvements of up to 38.80% compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a better way to predict how well a service will work (Quality of Service or QoS). Right now, services are not very good at this because they don’t understand the relationships between people and services. The authors want to fix this by creating a new method called TOGCL that takes into account these relationships and can learn from past experiences. They think this is important for making sure people have a good experience with services. To do this, TOGCL uses a special kind of graph that shows how users and services interact over time. It also has a way to look at the patterns in these interactions to make predictions about QoS. The authors tested their method on some data and it worked better than other methods. |
Keywords
* Artificial intelligence * Encoder * Graph attention network * Prompt * Transformer
