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SILTD: Structural Information for LLM-Generated Text Detection

文献类型：期刊论文

作者Yang, Jing1,2; Wang, Shi1,2; Zi, Kangli1,2; Sun, Yanshun1,2; Huang, Yuwei1,2; Luo, Tianyu1,2
刊名INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
出版日期2025-05-23
页码16
关键词LLM-generated text detection Structural information Multi-relational graph Clustering
ISSN号1868-8071
DOI10.1007/s13042-025-02616-x
英文摘要The rapid development of large language models (LLMs) has significantly improved the quality and diversity of AI-generated content(AIGC). LLM-Generated text detection plays an important role in preventing the harmful misuse of large language models. Existing approaches primarily analyze texts individually, overlooking the structural relationships between them. This limitation restricts their ability to generalize across diverse LLMs, as they fail to capture the shared statistical patterns inherent in generated texts. To address this, an unsupervised-based structural information for LLM-generated text detection (SILTD) method is proposed. The key insight is that texts from different LLMs exhibit latent similarities in their generative statistical space, which can be modeled to improve cross-model generalization. First, we construct a multi-relational text graph based on the similarity of text features, which aims to model the intricate similarities and correlations between texts. Second, we propose a novel unsupervised graph clustering method. The multi-relational graph is transformed into an encoding tree, which is then optimized based on a two-dimensional structure entropy minimization algorithm to achieve hierarchical clustering of texts. Structural entropy minimization enables achieving high-quality clusters, by measuring the uncertainty of random walks within the graph. Finally, we introduce a new method that measures text similarity and computes the intensity of text aggregation within each cluster, to perform in-cluster label inference. Extensive experiments show that, compared to baseline methods, our approach is more effective and generalizable in detecting six popular LLMs across five datasets.
资助项目National Key Research and Development Program of China[2022YFC3302300] ; Advanced Research Project[7090201050307] ; National 242 Information Security Program[2023A105]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001493346900001
出版者SPRINGER HEIDELBERG
源URL[http://119.78.100.204/handle/2XEOYT63/42401]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Wang, Shi; Zi, Kangli
作者单位1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Yang, Jing,Wang, Shi,Zi, Kangli,et al. SILTD: Structural Information for LLM-Generated Text Detection[J]. INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS,2025:16.
APA Yang, Jing,Wang, Shi,Zi, Kangli,Sun, Yanshun,Huang, Yuwei,&Luo, Tianyu.(2025).SILTD: Structural Information for LLM-Generated Text Detection.INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS,16.
MLA Yang, Jing,et al."SILTD: Structural Information for LLM-Generated Text Detection".INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS (2025):16.

入库方式： OAI收割

来源：计算技术研究所

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