多层次上下文学习使用大型语言模型用于社交网络上的文本归因图表.
Xiaokang Cai1, Ruoyuan Gong1, Hao Jiang1
1Electronic Information School, Wuhan University, Wuhan 430072, China.
Entropy (Basel, Switzerland)
|March 28, 2025
概括
多层次上下文学习者 (MCL) 模型通过整合多层次上下文来增强社交网络的大型语言模型 (LLM). 这种方法显著改善了图形神经网络 (GNN) 的语义嵌入.
科学领域:
- 社交网络分析分析
- 图形表示学习学习学习图形表示.
- 自然语言处理 (NLP) 是一种自然语言处理.
背景情况:
- 社交网络包含复杂的图形结构和丰富的文本信息.
- 现有的文本归属图 (TAG) 方法往往无法捕捉多层次上下文,导致信息丢失.
- 当前的框架将图形结构传达给大语言模型 (LLM),用于语义嵌入,但缺乏全面的上下文.
研究的目的:
- 引入一种新型模式,即多层次上下文学习者 (MCL),用于增强LLM在社交网络上的语义嵌入能力.
- 解决现有方法在社交网络数据中捕捉多层次上下文方面的局限性.
- 改进下游图形神经网络 (GNN) 任务的表示学习.
主要方法:
- 将社交网络建模为一个多层次的文本边缘图 (MC-TEG),以捕捉结构和语义关系.
- 利用LLM的推理能力,通过整合多层次上下文来产生语义嵌入.
- 采用定制的双向动态图的注意层来完善重量信息.
主要成果:
- 在6个真实世界的社交网络数据集中,MCL模型始终超过所有基线模型.
- 实现了显著的预测准确性改进,绝对收益在6.11%至11.05%之间,相比下一个最佳模型.
- 通过有效利用多层次的上下文信息,在生成语义嵌入方面表现出卓越的性能.
结论:
- 拟议的MCL模型有效地增强了用于社交网络分析的语义嵌入.
- 整合多层次上下文对于改善文本归属图形中的表示学习至关重要.
- MCL模型为社交网络上的各种下游GNN任务提供了一个有希望的方法.
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