TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP (2020.emnlp-demos)
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| Challenge: | TextAttack provides implementations of 16 adversarial attacks from the literature and supports a variety of models and datasets. |
| Approach: | They introduce a Python framework for adversarial attacks, data augmentation, and adversarially training in NLP. |
| Outcome: | This paper introduces a Python framework for adversarial attacks, data augmentation, and adversarially training in NLP. |
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Guoyang Zeng, Fanchao Qi, Qianrui Zhou, Tingji Zhang, Zixian Ma, Bairu Hou, Yuan Zang, Zhiyuan Liu, Maosong Sun
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| Challenge: | Existing approaches to building effective adversarial attackers focus on classification problems. |
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BERT-ATTACK: Adversarial Attack Against BERT Using BERT (2020.emnlp-main)
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| Challenge: | Current approaches to generate adversarial samples for discrete data are heuristic replacement strategies that are difficult to implement in continuous data. |
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| Challenge: | Recent methods for generating NLP adversarial examples involve combinatorial search and expensive sentence encoders for constraining the generated instances. |
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Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution (2021.emnlp-main)
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Zongyi Li, Jianhan Xu, Jiehang Zeng, Linyang Li, Xiaoqing Zheng, Qi Zhang, Kai-Wei Chang, Cho-Jui Hsieh
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Word-level Textual Adversarial Attacking as Combinatorial Optimization (2020.acl-main)
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| Challenge: | Existing word-level attack models are far from perfect because of unsuitable search space reduction methods and inefficient optimization algorithms. |
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Using Punctuation as an Adversarial Attack on Deep Learning-Based NLP Systems: An Empirical Study (2023.findings-eacl)
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| Challenge: | Existing studies show that insertions of a few symbols are a general attack mechanism, but grammar checks do not mitigate them. |
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| Challenge: | Recent research has focused on adversarial text attacks on neural networks for natural language processing. |
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Adversarial Text Normalization (2022.naacl-industry)
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| Challenge: | Text-based adversarial attacks are becoming more commonplace and accessible to general internet users. |
| Approach: | They propose a method that restores baseline performance on attacked content with low computational overhead. |
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LogicAttack: Adversarial Attacks for Evaluating Logical Consistency of Natural Language Inference (2023.findings-emnlp)
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| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated impressive performance on Natural Language Inference (NLI) tasks. |
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