LTD: Low Temperature Distillation for Robust Adversarial Training
Paper
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2111.02331
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Published
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1
Adversarial
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Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Too Much Accuracy
Paper • 1906.06784 • Published • 1 -
Pruning Adversarially Robust Neural Networks without Adversarial Examples
Paper • 2210.04311 • Published • 1 -
Mitigating the Accuracy-Robustness Trade-off via Multi-Teacher Adversarial Distillation
Paper • 2306.16170 • Published • 1 -
Mutual Adversarial Training: Learning together is better than going alone
Paper • 2112.05005 • Published • 1 -
Towards Adversarially Robust Continual Learning
Paper • 2303.17764 • Published • 1 -
Privacy-Preserving Prompt Tuning for Large Language Model Services
Paper • 2305.06212 • Published • 1 -
Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!
Paper • 2310.03693 • Published • 1 -
Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignment
Paper • 2308.09662 • Published • 3 -
PromptBench: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts
Paper • 2306.04528 • Published • 3 -
On the Adversarial Robustness of Mixture of Experts
Paper • 2210.10253 • Published • 1 -
CodeAttack: Code-Based Adversarial Attacks for Pre-trained Programming Language Models
Paper • 2206.00052 • Published • 1 -
Fake Alignment: Are LLMs Really Aligned Well?
Paper • 2311.05915 • Published • 2 -
Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5?
Paper • 2311.07587 • Published • 3