大型语言模型的以图形为导向的指令调整用于通用图形挖掘
IEEE transactions on pattern analysis and machine intelligence
|August 26, 2025
概括
MuseGraph集成了图形神经网络 (GNN) 和大型语言模型 (LLM) 以实现多功能图形挖掘. 这种基础模型可以在不同的图表任务和数据集中提高准确性,而无需对特定任务进行重新培训.
科学领域:
- 人工智能
- 机器学习
- 数据科学
背景情况:
- 图形神经网络 (GNN) 传统上需要特定任务的再培训.
- 大型语言模型 (LLM) 是有前途的,但在通用图表挖掘中未得到充分探索.
- 需要一个统一的模型来处理各种图表任务和数据集.
研究的目的:
- 开发一个新的框架,MuseGraph,整合GNN和LLM用于多功能图表挖掘.
- 使单个模型能够同时处理多个图表任务和数据集.
- 提高LLM的生成能力,同时提高图形挖掘性能.
主要方法:
- 开发了语言代码效率的紧图形描述.
- 提出了一个多元化的指令生成机制,用于提炼LLM推理的思维链 (CoT).
- 设计了一个以图表为基础的指令调整策略,以防止灾难性的遗忘,并促进相互增强.
主要成果:
- 在5个图表任务和10个数据集中,MuseGraph表现出显著的改进.
- 该框架提高了以图表为导向的下游任务的准确性.
- 随着图表挖掘的表现,还观察到更好的LLM生成能力.
结论:
- MuseGraph提供了一个通用图表挖掘的强大基础模型.
- 整合GNN和LLM为未来的研究提供了一个有希望的方向.
- 这种方法解决了传统GNN的局限性,并扩大了LLM的应用.
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