Defending Pre-trained Language Models from Adversarial Word Substitution Without Performance Sacrifice

Author:

Bao Rongzhou,Wang Jiayi,Zhao Hai

Publisher

Association for Computational Linguistics

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. From Attack to Defense;Innovations, Securities, and Case Studies Across Healthcare, Business, and Technology;2024-04-12

2. Text-Defend: Detecting Adversarial Examples using Local Outlier Factor;2023 IEEE 17th International Conference on Semantic Computing (ICSC);2023-02

3. Adversarial Attack and Defense on Natural Language Processing in Deep Learning: A Survey and Perspective;Machine Learning for Cyber Security;2023

4. LDM: A Location Detection Method of Emotional Adversarial Samples Attack for Emotion-Cause Pair Extraction;Lecture Notes on Data Engineering and Communications Technologies;2023

5. Proactive Detection of Query-based Adversarial Scenarios in NLP Systems;Proceedings of the 15th ACM Workshop on Artificial Intelligence and Security;2022-11-07

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