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新PromptGFM模型将LLM和GNN集成用于文本属性图

研究人员推出PromptGFM,这是一种新颖的文本属性图(TAGs)图基础模型(GFM)。该模型旨在通过解决现有解耦架构的局限性来改进大型语言模型(LLMs)和图神经网络(GNNs)的集成。PromptGFM包含一个图理解模块,该模块提示LLMs在文本空间中执行GNN工作流;以及一个图推理模块,该模块建立了一个基于语言的图词汇表,以增强表达能力和可迁移性。 AI

影响 这项研究通过改进LLM和GNN的集成,可能导致更有效的图基础模型,从而提高涉及文本属性图的任务的性能。

排序理由 该集群包含一篇详细介绍图基础模型新模型架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新PromptGFM模型将LLM和GNN集成用于文本属性图

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该集群包含一篇详细介绍图基础模型新模型架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Xi Zhu, Haochen Xue, Ziwei Zhao, Wujiang Xu, Jingyuan Huang, Minghao Guo, Qifan Wang, Kaixiong Zhou, Imran Razzak, Yongfeng Zhang ·

    LLM作为GNN:文本属性图基础模型的图词汇学习

    arXiv:2503.03313v4 Announce Type: replace-cross Abstract: Text-Attributed Graphs (TAGs), where each node is associated with text descriptions, are ubiquitous in real-world scenarios. They typically exhibit distinctive structure and domain-specific knowledge, motivating the develo…