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GraphTranslator0323.pdf

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1、GraphTranslator:Aligning Graph Model to Large Language Model for Open-ended TasksCheng YBeijing University of Posts and TelecommunicationsOutline Background:Graph Founda1on Model Our Recent A9empts Future Direc1onsOutline Background:Graph Founda1on Model Our Recent A9empts Future Direc1onsFounda,on

2、Models“A foundation model is any model that is trained on broad data and can be adapted to a wide range of downstream tasks.”11 R.Bommasani,D.A.Hudson,E.Adeli,R.Altman,S.Arora,S.von Arx,M.S.Bernstein,J.Bohg,A.Bosselut,E.Brun-skill,et al.,“On the opportunities and risks of foundation models,”arXiv pr

3、eprint arXiv:2108.07258,2021LanguageUSMGPT4Foundation models have become a reality in language,vision,and speech.VisionSpeechCharacteris,cs of Founda,on Models Emergence suggests that as a founda1on model scales up,it may spontaneously manifest novel capabili1es.2 Homogeniza1on alludes to the models

4、 versa1lity,enabling its deployment across diverse applica1ons.Machine Transla-onQ&AText Genera-onInforma-on Extrac-onHomogeniza-onFounda-on ModelEmergence2 Wei J,Tay Y,Bommasani R,et al.Emergent abilities of large language modelsJ.arXiv preprint arXiv:2206.07682,2022.Large Language Models(LLMs)With

5、 billions of parameters,LLMs have shown abilities towards artificial general intelligence(AGI),e.g.,understanding,reasoning,planning,etc.3 Zhao W X,Zhou K,Li J,et al.A survey of large language modelsJ.arXiv preprint arXiv:2303.18223,2023.On the other handGraph(network)is a common language for descri

6、bing relational data.Citation NetworkSocial NetworkMolecule GraphUser-item GraphInternetDrug Interaction GraphA History of Graph Theory&Learning17361950s1990s2000sGraph TheoryEulers seven bridgesGraph AlgorithmDijkstras shortest pathGraph ModelsRandom graph,Stochas-c block model,Scale-free network20

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本文主要探讨了图模型(Graph Models)与大型语言模型(Large Language Models, LLMs)的结合,提出了GraphTranslator框架,旨在桥接图模型和语言模型之间的差距,以处理开放性任务。核心数据包括:图理论的发展历程,图神经网络的演变,以及图模型与语言模型的结合。关键点如下: 1. 图模型是任何在广泛数据上训练,并能适应广泛下游任务的模型。 2. 大型语言模型拥有数十亿参数,展现出向人工智能(AGI)迈进的能力。 3. 图(网络)是描述关系数据的一种通用语言。 4. 现有工作在探索如何构建图模型(GFM),但尚未有明确的解决方案。 5. GraphTranslator通过将图表示转换为语言模型能理解的token序列,来实现图模型与语言模型的结合。 6. 实验表明,GraphTranslator在淘宝和ArXiv数据集上的零样本场景中表现出色。 7. 未来的研究方向包括:数据量与质量的提升,模型架构与学习范式的创新,应用与评估,以及安全隐私问题。
"图神经网络如何与大型语言模型结合?" "如何通过图翻译器提高零样本学习的性能?" "图神经网络在开放任务中的未来发展方向是什么?"
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