Modeling Adversarial Attack on Pre-trained Language Models as Sequential Decision Making (2023.findings-acl)
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| Challenge: | Pre-trained language models (PLMs) have shown strong potential in various downstream tasks. |
| Approach: | They propose to model adversarial attack task as a sequential decision-making problem where the whole attack process is sequential with two decision- making problems, i.e., word finder and word substitution. |
| Outcome: | The proposed approach achieves the highest attack success rate with a comparable modification rate and semantic similarity to attack fine-tuned BERT. |
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| Challenge: | Existing approaches to building effective adversarial attackers focus on classification problems. |
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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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Recent Advances in Pre-trained Language Models: Why Do They Work and How Do They Work (2022.aacl-tutorials)
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| Challenge: | Pre-trained language models are language models that are pre-taught on large-scaled corpora in a self-supervised fashion. |
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Enhancing Neural Models with Vulnerability via Adversarial Attack (2020.coling-main)
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XVD: Cross-Vocabulary Differentiable Training for Generative Adversarial Attacks (2024.lrec-main)
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| Challenge: | Existing approaches to create adversarial examples using tokens are not sufficient to ensure other desirable properties such as similarity to non-adversarial examples, linguistic fluency, and so forth. |
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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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Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models (2021.acl-short)
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| Challenge: | Pre-trained language models have achieved human-level performance on many Machine Reading Comprehension (MRC) tasks, but it remains unclear whether these models truly understand language or answer questions by exploiting statistical biases in datasets. |
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Are Language Models Agnostic to Linguistically Grounded Perturbations? A Case Study of Indic Languages (2025.findings-naacl)
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| Challenge: | Existing studies do not focus on linguistically grounded attacks, but pre-trained models are susceptible to these perturbations. |
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Reevaluating Adversarial Examples in Natural Language (2020.findings-emnlp)
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| Challenge: | State-of-the-art adversarial examples lack a common definition of what constitutes success . human surveys show that to preserve semantics, we need to increase the minimum cosine similarities between the embeddings of swapped words and between the sentence encodings of original and perturbed sentences. |
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Prompt Tuning for Discriminative Pre-trained Language Models (2022.findings-acl)
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Yuan Yao, Bowen Dong, Ao Zhang, Zhengyan Zhang, Ruobing Xie, Zhiyuan Liu, Leyu Lin, Maosong Sun, Jianyong Wang
| Challenge: | Recent studies have shown promising results of prompt tuning in stimulating pre-trained language models (PLMs) for natural language processing tasks. |
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