Summary of Subgraph-level Universal Prompt Tuning, by Junhyun Lee et al.
Subgraph-level Universal Prompt Tuning
by Junhyun Lee, Wooseong Yang, Jaewoo Kang
First submitted to arxiv on: 16 Feb 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 This paper introduces a new approach to prompt tuning for graph neural networks, addressing the limitations of previous methods that are tailored to specific pre-training strategies. The proposed Subgraph-level Universal Prompt Tuning (SUPT) method focuses on the detailed context within subgraphs and assigns prompt features at this level, allowing it to be universally applicable. The paper demonstrates the effectiveness of SUPT in both full-shot and few-shot scenario experiments, outperforming fine-tuning-based methods in most cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research makes graph neural networks more powerful by finding a better way to adapt pre-trained models using prompts. Currently, there are only limited ways to do this, which work well for some types of graphs but not others. The new method, called SUPT, can be used with any type of graph and is very good at adapting models to specific parts of the graph. This means it can be used in many different situations where graph neural networks are useful. |
Keywords
* Artificial intelligence * Few shot * Fine tuning * Prompt
