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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">TACS</journal-id><journal-title-group><journal-title>Technology and Application of Computer Science</journal-title></journal-title-group><issn>2998-8926</issn><eissn>2998-8934</eissn><publisher><publisher-name>Art and Technology</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.61369/TACS.2025100014</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>基于BERT-CNN-LSTM 的大语言模型提示词安全分类方法研究</title><url>https://artdesignp.com/journal/TACS/2/10/10.61369/TACS.2025100014</url><author>李强,顾文君,吴昱昊,卢杨凡</author><pub-date pub-type="publication-year"><year>2025</year></pub-date><volume>2</volume><issue>10</issue><history><date date-type="pub"><published-time>2025-05-28</published-time></date></history><abstract/><keywords>大模型安全,多模型融合,提示词工程,风险治理</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1] 姜毅, 杨勇, 印佳丽等. 大语言模型安全与隐私风险综述[J]. 计算机研究与发展,2025,62(8):1979-2018.[2] Tao C, Shen T, Gao S, et al., LLMs are Also Effective Embedding Models: An In - depth Overview[J]. arXiv preprint arXiv:2412.12591, 2024.[3] 赵鸿山, 范贵生, 虞慧群. 基于归一化文档频率的文本分类特征选择方法[J]. 华东理工大学学报( 自然科学版),2019,45(5):809-814.[4] FU X, WEI Y, XU F, et al. Semi-supervised aspect-level sentiment classification model based on variational autoen coder[J]. Knowledge-Based Systems, 2019, 171:81-92.[5] 李明, 刘磊, 王宏志.CBLGA 和CBLCA 混合模型用于长文本和短文本的分类[J]. 吉林大学学报( 信息科学版),2021,39(3):285-292.[6] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Neural Information Processing Systems(NIPS). USA: MIT Press, 2017: 6000-6010.[7] Adhikari A, Ram A, Tang R, et al., DocBERT: BERT for Document Classification[J]. arXiv preprint arXiv:1904.08398, 2019.[8] Minaee S, Kalchbrenner N, Cambria E, et al., Deep learning based text classification: A comprehensive review [J]. ACM Computing Surveys,2021,54 (3):62.[9] Iz Beltagy, Matthew E. Peters, Arman Cohan. Longformer: The Long-Document Transformer[J]. Transactions of the Association for Computational Linguistics, 2020, 8:171-185.[10] R Pappagari, P Zelasko, J Villalba, et al., "Hierarchical transformers for long document classification," in 2019 IEEE Automatic Speech Recognition and UnderstandingWorkshop (ASRU), Singapore: IEEE, 2019, pp. 814-821.[11]S Khandve, V Wagh, A Wani, et al., "Hierarchical neural network approaches for long document classification," in Proc. 14th Int. Conf. Mach. Learn. Comput.,Guangzhou, China: ACM, 2022, pp. 52-59, DOI: 10.1145/3529836.3529901.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
