在大图体上进行预训练可以帮助多个图表应用程序

相关实验视频

Updated: Jul 2, 2025

03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

558

Han Xie¹, Vassilis N Ioannidis², Carl Yang¹

¹Emory University Atlanta, GA, USA.

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining

|February 20, 2024

概括

本研究介绍了对异质图的图形意识语言模型预训 (GaLM). 通过利用文本和图形结构,GaLM有效地增强了下游图形应用程序.

科学领域:

人工智能的人工智能
自然语言处理自然语言处理.
图表采矿图表采矿

背景情况:

对大型文本体的模型预训练对于自然语言处理 (NLP) 任务是有效的.
在大型图形上预训练图形模型显示出下游图形应用的前景.
关于在具有丰富文本数据的大型异质图表上进行预训练模型的研究有限.

研究的目的:

提出一个新的框架,用于图形意识语言模型预训练 (GaLM) 在大型图形 corpora.
在各种下游应用中研究预训练模型的微调,使用不同的图形图表.
解决预培训模型中的差距,这些模型整合了来自异质来源的文本和图形信息.

主要方法:

开发了一个图形意识的语言模型预培训 (GaLM) 框架.
集成的大型语言模型 (LLM) 与图形神经网络 (GNN).
在不同的图表图表上,在下游任务中采用各种微调策略.

主要成果:

通过对现实世界和公共数据集的广泛实验,证明了GaLM的有效性.
经验结果表明,拟议的预培训方法具有显著的好处.
深入分析提供了有价值的见解和从实验中吸取的经验教训.

结论:

拟议的图形意识语言模型预训练 (GaLM) 框架对异质图形数据有效.
在各种下游图表应用中,GaLM提高了性能.
这项研究为图形挖掘和NLP的预训练模型提供了新的方向.

关键词:

图形神经网络的神经网络不同质的图形不同质的图形大型语言模型前期培训和微调工作

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