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A prompt-based approach to adversarial example generation and robustness enhancement

文献类型：期刊论文

作者Yang, Yuting4,5; Huang, Pei3; Cao, Juan4,5; Li, Jintao5; Lin, Yun2; Ma, Feifei1,4
刊名FRONTIERS OF COMPUTER SCIENCE
出版日期2024-08-01
卷号18期号:4页码:12
关键词robustness adversarial example prompt learning pre-trained language model
ISSN号2095-2228
DOI10.1007/s11704-023-2639-2
英文摘要Recent years have seen the wide application of natural language processing (NLP) models in crucial areas such as finance, medical treatment, and news media, raising concerns about the model robustness and vulnerabilities. We find that prompt paradigm can probe special robust defects of pre-trained language models. Malicious prompt texts are first constructed for inputs and a pre-trained language model can generate adversarial examples for victim models via maskfilling. Experimental results show that prompt paradigm can efficiently generate more diverse adversarial examples besides synonym substitution. Then, we propose a novel robust training approach based on prompt paradigm which incorporates prompt texts as the alternatives to adversarial examples and enhances robustness under a lightweight minimax-style optimization framework. Experiments on three real-world tasks and two deep neural models show that our approach can significantly improve the robustness of models to resist adversarial attacks.
资助项目National Key R&D Program of China[2021AAA0140203] ; Zhejiang Provincial Key Research and Development Program of China[2021C01164] ; National Natural Science Foundation of China[61972384] ; National Natural Science Foundation of China[62132020] ; National Natural Science Foundation of China[62203425]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001130418100004
出版者HIGHER EDUCATION PRESS
源URL[http://119.78.100.204/handle/2XEOYT63/38409]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Cao, Juan; Ma, Feifei
作者单位1.Chinese Acad Sci, Inst Software, Lab Parallel Software & Computat Sci, Beijing 100190, Peoples R China
2.Natl Univ Singapore, Sch Comp, Singapore 119077, Singapore
3.Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA
4.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China
5.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, Yuting,Huang, Pei,Cao, Juan,et al. A prompt-based approach to adversarial example generation and robustness enhancement[J]. FRONTIERS OF COMPUTER SCIENCE,2024,18(4):12.
APA Yang, Yuting,Huang, Pei,Cao, Juan,Li, Jintao,Lin, Yun,&Ma, Feifei.(2024).A prompt-based approach to adversarial example generation and robustness enhancement.FRONTIERS OF COMPUTER SCIENCE,18(4),12.
MLA Yang, Yuting,et al."A prompt-based approach to adversarial example generation and robustness enhancement".FRONTIERS OF COMPUTER SCIENCE 18.4(2024):12.

入库方式： OAI收割

来源：计算技术研究所

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