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English(EN) Beyond the Golden Teacher: Enhancing Graph Learning through LLM-GNN Co-teaching

LLM-GNN协同教学提升少样本图学习准确性

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为LLM-GNN协同教学的新方法来改进少样本图学习。该方法避免指定一个模型作为“黄金教师”，而是允许图神经网络（GNN）和大型语言模型（LLM）协同学习。模型交换置信伪标签并相互更新，监督来自它们随时间的共识。这种协同教学框架在六个基准测试中持续优于先前的方法，在节点分类等任务中显示出准确性的显著提高。 AI

影响 增强了基于图的AI系统的少样本学习能力，可能提高推荐引擎和社交网络分析等领域的性能。

排序理由 提出一种新颖图学习方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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

LLM-GNN协同教学提升少样本图学习准确性

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提出一种新颖图学习方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zhuoyi Peng, Hanlin Gu, Lixin Fan, Yi Yang ·

    超越Golden Teacher：通过LLM-GNN协同教学增强图学习

    arXiv:2606.11583v1 Announce Type: new Abstract: Text-attributed graphs (TAGs) underlie real-world applications such as citation networks, social media, and e-commerce. Few-shot graph learning on TAGs is hard: with only a handful of labels per class and the rest of the graph unann…

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