Summary of All in One: Multi-task Prompting For Graph Neural Networks (extended Abstract), by Xiangguo Sun et al.
All in One: Multi-Task Prompting for Graph Neural Networks (Extended Abstract)
by Xiangguo Sun, Hong Cheng, Jia Li, Bo Liu, Jihong Guan
First submitted to arxiv on: 11 Mar 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 introduces a novel approach to bridging the gap between pre-trained graph models and diverse graph tasks, inspired by prompt learning in NLP. The proposed multi-task prompting method unifies graph and language prompt formats, enabling NLP’s prompting strategies to be adapted for graph tasks. By analyzing the task space of graph applications and reformulating problems to fit graph-level tasks, the method improves prompt initialization for multiple tasks using meta-learning. Experiments demonstrate the effectiveness of this approach in enhancing model performance across different graph tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem that makes it hard for pre-trained graph models to work well with many different types of graph tasks. The authors come up with a new way to help these models by giving them special instructions, or “prompts,” that are tailored to the specific task they’re trying to do. This helps the model learn more effectively and avoid mistakes. By using this approach, the model can perform better on many different types of graph tasks. |
Keywords
* Artificial intelligence * Meta learning * Multi task * Nlp * Prompt * Prompting
