Summary of Let’s Ask Gnn: Empowering Large Language Model For Graph In-context Learning, by Zhengyu Hu et al.
Let’s Ask GNN: Empowering Large Language Model for Graph In-Context Learning
by Zhengyu Hu, Yichuan Li, Zhengyu Chen, Jingang Wang, Han Liu, Kyumin Lee, Kaize Ding
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 AskGNN is a novel approach that bridges the gap between sequential text processing and graph-structured data by leveraging large language models (LLMs). The method employs In-Context Learning (ICL) to integrate graph data and task-specific information into LLMs. AskGNN uses a Graph Neural Network (GNN)-powered structure-enhanced retriever to select labeled nodes across graphs, incorporating complex graph structures and their supervision signals. The learning-to-retrieve algorithm optimizes the retriever to select example nodes that maximize LLM performance on graph tasks. Experimental results demonstrate AskGNN’s superior effectiveness in graph task performance, opening new avenues for applying LLMs to graph-structured data without extensive fine-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AskGNN is a new way to use large language models (LLMs) for complex systems that are described using graphs. Graphs are like maps that show relationships between things. The challenge is that LLMs are good at processing text, but not so good with graph data. AskGNN solves this problem by teaching the LLM how to understand and work with graphs. This helps the LLM make better decisions when working with complex systems. In tests, AskGNN did much better than other methods, showing that it can be a powerful tool for working with graph-structured data. |
Keywords
» Artificial intelligence » Fine tuning » Gnn » Graph neural network
