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LLM Features Can Harm GNN Performance on Homophilous Graphs

作者 PulseAugur 编辑部 · [2 个来源] ·

一篇新研究论文揭示，将大型语言模型（LLM）生成的特征整合到图神经网络（GNN）中，有时会降低在特定基准测试上的性能。这种被称为“连接干扰”的效应，在LLM特征被简单地附加到现有数据时被观察到，导致在PubMed和Cora等数据集上的准确性显著下降。研究表明，LLM特征的有效性取决于超越简单连接的因素，在中等同质性数据集上观察到了性能提升。 AI

影响 这项研究表明，简单地将LLM生成的特征附加到GNN上并不总能带来改进，甚至可能在某些图类型上降低性能。

排序理由 该集群包含一篇研究论文，详细介绍了关于LLM特征和GNN之间交互的新发现。

在 arXiv cs.CL 阅读 →

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LLM Features Can Harm GNN Performance on Homophilous Graphs

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该集群包含一篇研究论文，详细介绍了关于LLM特征和GNN之间交互的新发现。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Zhongyuan Wang, Pratyusha Vemuri ·

    LLM 特征会损害 GNN：同质图基准上的连接干扰

    arXiv:2606.17579v1 Announce Type: cross Abstract: Adding LLM-generated node features to graph neural networks (GNNs) is widely reported to improve accuracy on standard benchmarks. We document a contrasting observation: when LLM features are introduced through pure input concatena…

  2. arXiv cs.CL TIER_1 English(EN) · Pratyusha Vemuri ·

    LLM 特征会损害 GNN：同质图基准上的连接干扰

    Adding LLM-generated node features to graph neural networks (GNNs) is widely reported to improve accuracy on standard benchmarks. We document a contrasting observation: when LLM features are introduced through pure input concatenation (rather than joint training, distillation, or…

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