研究人员开发了一个新框架LGSPF，旨在利用大型语言模型（LLMs）和图神经网络（GNNs）来改进欺诈检测。该方法通过使用软提示将图结构与语义空间连接起来，解决了欺诈检测中有限文本数据的挑战，并避免了硬提示方法中常见的特征失真。LGSPF还包含一个并行的GNN编码器，用于将多关系图数据转换为令牌，以实现更好的LLM理解，在欺诈检测基准测试中取得了最先进的性能，并增强了语义可解释性。 AI
影响 该框架通过更好地利用LLMs的图结构，可以显著提高欺诈检测系统的准确性和可解释性。
排序理由 该集群包含一篇研究论文，详细介绍了使用LLMs和GNNs进行欺诈检测的新框架，该论文已提交至arXiv。
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arXiv:2605.28524v1 Announce Type: new Abstract: In recent years, Large Language Models (LLMs) have shown great capability in processing graph tasks such as fraud detection. However, most existing methods rely heavily on rich text attributes, which poses difficulties for this doma…
In recent years, Large Language Models (LLMs) have shown great capability in processing graph tasks such as fraud detection. However, most existing methods rely heavily on rich text attributes, which poses difficulties for this domain due to the lack of textual data. Although som…