ORTicket: Let One Robust BERT Ticket Transfer across Different Tasks (2024.lrec-main)
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| Challenge: | Pretrained language models are susceptible to subtle perturbations and require multiple adversarial training during fine-tuning to improve their robustness. |
| Approach: | They propose a novel adversarial defense method ORTicket that fine-tunes a model for downstream tasks. |
| Outcome: | The proposed method achieves comparable robustness to other defense methods while maintaining the efficiency of fine-tuning. |
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| Challenge: | Recent studies show pre-trained language models contain matching subnetworks that have similar transfer learning performance as the original PLM. |
| Approach: | They propose to prune matching subnetworks using magnitude-based pruning . they propose to optimize the subnetwork structure towards the pre-training objectives . |
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Efficient Adversarial Training with Robust Early-Bird Tickets (2022.emnlp-main)
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| Challenge: | Existing methods to improve the robustness of pre-trained language models are expensive because of the need to generate adversarial examples via gradient descent. |
| Approach: | They propose an adversarial optimization method that searches for robust tickets with structured sparsity in the early stage and fine-tunes tickets in the remaining time. |
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Robust Lottery Tickets for Pre-trained Language Models (2022.acl-long)
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| Challenge: | Recent studies have shown that pre-trained language models contain smaller matching subnetworks that are not robust to adversarial examples. |
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Robust Transfer Learning with Pretrained Language Models through Adapters (2021.acl-short)
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| Challenge: | Existing approaches to transfer learning with pretrained transformer-based language models are not robust and can be adversarial. |
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Model Extraction and Adversarial Transferability, Your BERT is Vulnerable! (2021.naacl-main)
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| Challenge: | Pretrained language models are used for natural language processing (NLP) but when they are deployed as a service, they can suffer from different attacks . |
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Flooding-X: Improving BERT’s Resistance to Adversarial Attacks via Loss-Restricted Fine-Tuning (2022.acl-long)
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Qin Liu, Rui Zheng, Bao Rong, Jingyi Liu, ZhiHua Liu, Zhanzhan Cheng, Liang Qiao, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing approaches to generating adversarial perturbations scale up the cost of training computational complexity by the number of gradient steps it takes to obtain the adversarials. |
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Pruning Unsafe Tickets: A Resource-Efficient Framework for Safer and More Robust LLMs (2026.acl-long)
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| Challenge: | Empirical evaluations on ML models show substantial reductions in unsafe generations and improved robustness against jailbreak attacks. |
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ROSE: Robust Selective Fine-tuning for Pre-trained Language Models (2022.emnlp-main)
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| Challenge: | Recent studies have highlighted the lack of adversarial robustness in pre-trained models. |
| Approach: | They propose a fine-tuning approach that conducts selective updates when adapting pre-trained models to downstream tasks. |
| Outcome: | The proposed approach improves adversarial robustness on downstream tasks . it eliminates spurious updates, leading to flatter and wider optima than the conventional method . |
PlugAT: A Plug and Play Module to Defend against Textual Adversarial Attack (2022.coling-1)
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| Challenge: | Recent studies have shown that adversarial examples can be easily fooled by adversarially perturbed examples. |
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SemRoDe: Macro Adversarial Training to Learn Representations that are Robust to Word-Level Attacks (2024.naacl-long)
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| Challenge: | Existing approaches to defend against word-level attacks have been limited. |
| Approach: | They propose a new approach called Semantic Robust Defence to enhance the robustness of language models by aligning the domains with a distance-based objective. |
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