Summary of Graphinstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability, by Zihan Luo et al.
GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability
by Zihan Luo, Xiran Song, Hong Huang, Jianxun Lian, Chenhao Zhang, Jinqi Jiang, Xing Xie
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 A new benchmark, GraphInstruct, is proposed to evaluate and enhance the graph understanding abilities of large language models (LLMs). The benchmark consists of 21 classical graph reasoning tasks with diverse graph generation pipelines and detailed reasoning steps. To leverage this benchmark, an instruction-tuned model called GraphLM is constructed, which demonstrates prominent graph understanding capabilities. Furthermore, a step mask training strategy is proposed to enhance the LLM’s graph reasoning abilities, resulting in the GraphLM+ model. Extensive experiments show that GraphLM and GraphLM+ outperform other LLMs, making them suitable for exploring the potential of LLMs in the graph data mining domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to test how well large language models understand graphs. A graph is like a map with nodes and connections between them. The researchers made a special test set called GraphInstruct that has 21 different tasks to help big language models get better at understanding graphs. They also made two new models, GraphLM and GraphLM+, which are really good at understanding graphs. This is important because it can help us use these language models for things like finding patterns in data. |
Keywords
» Artificial intelligence » Mask
