Summary of Bridging Large Language Models and Graph Structure Learning Models For Robust Representation Learning, by Guangxin Su et al.
Bridging Large Language Models and Graph Structure Learning Models for Robust Representation Learning
by Guangxin Su, Yifan Zhu, Wenjie Zhang, Hanchen Wang, Ying Zhang
First submitted to arxiv on: 15 Oct 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 LangGSL, a novel framework that combines pre-trained language models (LLMs) and graph structure learning models (GSLMs) to jointly enhance node feature and graph structure learning. By leveraging LLMs to filter noise in raw data and extract valuable features, LangGSL enhances the synergy of downstream models. The framework involves mutual learning between the LM and GSLM, with the LM generating pseudo-labels and informative node embeddings that are integrated into the GSLM’s prediction phase, while the GSLM refines the evolving graph structure constructed from the LM’s output. This approach results in enhanced node features and a more robust graph structure. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to learn about graphs, which is important for many real-world applications. Graphs are like maps that show how things are connected. But sometimes, these graphs can be messy and have lots of noise. To fix this problem, the researchers created a new framework called LangGSL that combines two types of models: language models and graph structure learning models. These models work together to make the graphs cleaner and more accurate. |
