Model-tuning Via Prompts Makes NLP Models Adversarially Robust (2023.emnlp-main)
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| Challenge: | Pre-trained models are typically adapted to downstream tasks by appending a randomly initialized multilayer perceptron to their topmost representation layer and fine-tuning the entire model on a downstream task. |
| Approach: | They propose to append a multilayer perceptron to a CLS token and fine-tune the entire model on a downstream task. |
| Outcome: | The proposed model-tuning via prompts outperforms adversarial training-based state-of-art defenses by 3.5% and improves against adversarials by 8% over standard methods. |
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| Challenge: | Existing prompt tuning methods have training instability issues due to large variance of scores . existing prompt tuning algorithms have training stability issues due a slight change of input data . |
| Approach: | They propose an algorithm that smooths the loss landscape of vanilla prompt tuning by perturbation-based regularizers. |
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| Challenge: | Prompt-based learning inherits the vulnerability from pre-training, where model predictions can be misled by inserting triggers into the text. |
| Approach: | They propose a potential solution to mitigate this vulnerability by injecting triggers into pre-trained language models using only plain text. |
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Parameter-free and Accessible Prompt Learning to Enhance Adversarial Robustness for Pre-trained Vision-Language Models (2025.naacl-long)
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| Challenge: | Large pre-trained Vision-Language Models (VLMs) have revolutionized downstream vision-language tasks including classification, object detection, and segmentation. |
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The Power of Scale for Parameter-Efficient Prompt Tuning (2021.emnlp-main)
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| Challenge: | Unlike discrete text prompts used by GPT-3, soft prompts are learned through backpropagation and can be tuned to incorporate signals from any number of labeled examples. |
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| Challenge: | Recent studies have highlighted the lack of adversarial robustness in pre-trained models. |
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LinkPrompt: Natural and Universal Adversarial Attacks on Prompt-based Language Models (2024.naacl-long)
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| Challenge: | Prompt-based learning is a new language model training paradigm that adapts Pre-trained Language Models (PLMs) to downstream tasks. |
| Approach: | They propose a prompt-based learning paradigm that adapts Pre-trained Language Models to downstream tasks . they use a gradient-based beam search algorithm to generate adversarial triggers . |
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On the Relationship between Skill Neurons and Robustness in Prompt Tuning (2024.lrec-main)
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| Challenge: | Prompt Tuning is a parameter-efficient finetuning method for pre-trained large language models (PLMs). |
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PromptFix: Few-shot Backdoor Removal via Adversarial Prompt Tuning (2024.naacl-long)
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| Challenge: | Existing studies have shown that pre-trained language models can be backdoored such that model behavior is manipulated when trigger tokens are presented. |
| Approach: | They propose a backdoor mitigation strategy for NLP models via adversarial prompt-tuning in few-shot settings that uses two extra sets of soft tokens which approximate the trigger and counteract it respectively. |
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Reliable Gradient-free and Likelihood-free Prompt Tuning (2023.findings-eacl)
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| Challenge: | Large pre-trained language models are often offered as black-box APIs due to privacy or commercial constraints. |
| Approach: | They propose to tune the soft prompts without requiring gradient computation and extend the model to include a distribution over prompts. |
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Adversarial Robustness of Prompt-based Few-Shot Learning for Natural Language Understanding (2023.findings-acl)
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| Challenge: | Recent few-shot learning methods focus on improving downstream task performance, but there is limited understanding of the adversarial robustness of such methods. |
| Approach: | They evaluate prompt-based FSL methods against fully fine-tuned models to better understand the impact of various factors towards robustness. |
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