Summary of Parameter-efficient Tuning Large Language Models For Graph Representation Learning, by Qi Zhu et al.
Parameter-Efficient Tuning Large Language Models for Graph Representation Learning
by Qi Zhu, Da Zheng, Xiang Song, Shichang Zhang, Bowen Jin, Yizhou Sun, George Karypis
First submitted to arxiv on: 28 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 The paper introduces Graph-aware Parameter-Efficient Fine-Tuning (GPEFT), a novel approach for efficient graph representation learning using Large Language Models (LLMs) on text-rich graphs. GPEFT combines a graph neural network (GNN) to encode structural information with an LLM to generate node embeddings. The authors validate their method through experiments on 8 different text-rich graphs, achieving an average improvement of 2% in hit@1 and Mean Reciprocal Rank (MRR) in link prediction evaluations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses Large Language Models to learn graph representations more efficiently. It’s about using language models to help with graph problems. The authors make a new way to do this that’s faster and better than before. They test it on many different kinds of graphs and show it works really well. |
Keywords
» Artificial intelligence » Fine tuning » Gnn » Graph neural network » Parameter efficient » Representation learning
