arXiv 2504.021462025-04-02cs.CLcs.LG

LL4G：基于图的性格检测的自监督动态优化

LL4G: Self-Supervised Dynamic Optimization for Graph-Based Personality Detection

University of Southampton（南安普顿大学）；University of Essex（埃塞克斯大学）；Birkbeck, University of London（伦敦大学伯克贝克学院）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）

Lingzhi Shen, Yunfei Long, Xiaohao Cai, Guanming Chen, Yuhan Wang, Imran Razzak, Shoaib Jameel

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AI总结：

本文提出自监督框架LL4G，利用大语言模型提取语义特征并推断关系以动态优化图结构，结合图神经网络进行联合训练，在Kaggle和Pandora数据集上优于现有模型，生成稳健的性格特征。

摘要：

基于图的性格检测从文本数据（特别是社交媒体帖子）中构建图结构。现有方法常受限于稀疏或噪声数据，且依赖静态图，难以捕捉节点与关系间的动态变化。本文提出LL4G，一个利用大语言模型（LLMs）优化图神经网络（GNNs）的自监督框架。LLMs提取丰富的语义特征以生成节点表示，并推断显式与隐式关系。图结构根据输入数据自适应添加节点和边，持续自我优化。随后GNN利用这些优化表示，在节点重构、边预测和对比学习任务上进行联合训练。语义与结构信息的整合生成了稳健的性格特征。在Kaggle和Pandora数据集上的实验表明，LL4G优于现有最先进模型。

英文摘要：

Graph-based personality detection constructs graph structures from textual data, particularly social media posts. Current methods often struggle with sparse or noisy data and rely on static graphs, limiting their ability to capture dynamic changes between nodes and relationships. This paper introduces LL4G, a self-supervised framework leveraging large language models (LLMs) to optimize graph neural networks (GNNs). LLMs extract rich semantic features to generate node representations and to infer explicit and implicit relationships. The graph structure adaptively adds nodes and edges based on input data, continuously optimizing itself. The GNN then uses these optimized representations for joint training on node reconstruction, edge prediction, and contrastive learning tasks. This integration of semantic and structural information generates robust personality profiles. Experimental results on Kaggle and Pandora datasets show LL4G outperforms state-of-the-art models.

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