Summary of Protransformer: Robustify Transformers Via Plug-and-play Paradigm, by Zhichao Hou et al.
ProTransformer: Robustify Transformers via Plug-and-Play Paradigm
by Zhichao Hou, Weizhi Gao, Yuchen Shen, Feiyi Wang, Xiaorui Liu
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Cryptography and Security (cs.CR)
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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 The novel ProTransformer architecture introduces a robust attention mechanism designed to enhance the resilience of transformer-based models without requiring additional training or fine-tuning. This plug-and-play layer can be integrated into existing transformers, improving their robustness across various prediction tasks, attack mechanisms, backbone architectures, and data domains. Experimental results show that ProTransformer consistently improves vanilla transformer performance by 11-28% against TextFooler attacks, and demonstrates promising resilience in large language models (LLMs) against prompting-based attacks. The ProTransformer also exhibits outstanding robustness in vision and graph domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ProTransformer is a new way to make transformer-based machines better at handling tricky situations. It’s like adding armor to a superhero! This special attention mechanism can be easily added to existing transformers, making them more resilient without needing extra training or tuning. The results show that ProTransformer makes big improvements (up to 28%) when faced with attacks designed to mess things up. It also helps large language models and works well in vision and graph domains. |
Keywords
» Artificial intelligence » Attention » Fine tuning » Prompting » Transformer
