Summary of Musegraph: Graph-oriented Instruction Tuning Of Large Language Models For Generic Graph Mining, by Yanchao Tan et al.
MuseGraph: Graph-oriented Instruction Tuning of Large Language Models for Generic Graph Mining
by Yanchao Tan, Hang Lv, Xinyi Huang, Jiawei Zhang, Shiping Wang, Carl Yang
First submitted to arxiv on: 2 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 This paper proposes a novel framework called MuseGraph that integrates Graph Neural Networks (GNNs) and Large Language Models (LLMs) to facilitate a more effective and generic approach for graph mining. The framework is designed to handle attributed graphs and can be applied to different tasks and datasets without requiring re-training. MuseGraph consists of three main components: an adaptive input generation module that encapsulates key information from the graph, a diverse instruction generation mechanism that distills reasoning capabilities from LLMs, and a graph-aware instruction tuning strategy that allocates task-specific instruction packages across tasks and datasets. Experimental results demonstrate significant improvements in different graph tasks, highlighting the potential of MuseGraph in enhancing the accuracy of graph-oriented downstream tasks while leveraging the generative powers of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MuseGraph is a new way to use computers to understand and work with complex networks like social media or internet data. Right now, we have special computer programs called Graph Neural Networks (GNNs) that can analyze these networks. But GNNs need to be re-trained every time they’re used for something different. MuseGraph combines GNNs with another type of computer program called Large Language Models (LLMs) to make it easier and more accurate to work with networks. This new framework has three parts: one that makes a summary of the graph, one that creates instructions based on what we want to do with the graph, and one that adjusts those instructions so they work well across different tasks. The results show that MuseGraph can really improve how well computers do certain tasks when working with networks. |
Keywords
» Artificial intelligence » Instruction tuning
