Summary of Promptfix: Few-shot Backdoor Removal Via Adversarial Prompt Tuning, by Tianrong Zhang et al.
PromptFix: Few-shot Backdoor Removal via Adversarial Prompt Tuning
by Tianrong Zhang, Zhaohan Xi, Ting Wang, Prasenjit Mitra, Jinghui Chen
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper proposes PromptFix, a novel strategy for mitigating backdoors in natural language processing (NLP) models via adversarial prompt-tuning. Backdoors are manipulated model behaviors triggered by specific tokens. Existing methods rely on fine-tuning and accurate trigger inversion, whereas PromptFix keeps the model parameters intact and uses soft tokens to approximate triggers and counteract them. This approach eliminates the need for enumerating backdoor configurations and enables adaptive balance between trigger finding and performance preservation. The paper demonstrates the effectiveness of PromptFix against various backdoor attacks and its applicability to models pretrained on unknown data sources, which is common in prompt tuning scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Researchers have found that some language models can be tricked into doing things they shouldn’t do when certain words are used. This is called a “backdoor.” To fix this problem, the authors of this paper suggest a new way to train these models so they won’t fall for tricks. Instead of changing the model itself, they use special tokens that help cancel out the trickery. This method works well and can be used even if the model was trained on data from an unknown source. |
Keywords
» Artificial intelligence » Fine tuning » Natural language processing » Nlp » Prompt
