Summary of Partially Recentralization Softmax Loss For Vision-language Models Robustness, by Hao Wang et al.
Partially Recentralization Softmax Loss for Vision-Language Models Robustness
by Hao Wang, Jinzhe Jiang, Xin Zhang, Chen Li
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 In a breakthrough for natural language processing tasks, multimodal techniques have gained popularity. However, they are vulnerable to adversarial attacks that dramatically change model outputs with small input perturbations. Despite defense techniques in computer vision and NLP models, the robustness of multimodal models remains unexplored. This paper investigates modifying pre-trained multimodal loss functions by restricting top K softmax outputs for improved adversarial robustness against popular attacks. Our experiments show significant improvement through fine-tuning. Future research should explore output diversity, generalization, and the robustness-performance trade-off of this loss function. We will release our code after acceptance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting better at understanding natural languages. But, they’re also very sensitive to small changes in what we give them as input. This makes it easy for someone to trick the model into giving a wrong answer. To fix this, researchers have tried different ways to make the model more robust. In this paper, scientists looked at how changing the way the model calculates its answers can help make it less susceptible to these tricks. They found that by tweaking the calculations, they could make the model much better at resisting these attacks. This is an important area of study because as AI gets better and better, we need to make sure it’s not too easy to manipulate. |
Keywords
» Artificial intelligence » Fine tuning » Generalization » Loss function » Natural language processing » Nlp » Softmax
