Continuous adversarial training in the embedding space produces a robust generalization bound for linear transformers that decreases with perturbation radius, tied to singular values of the embedding matrix, and motivates a new regularizer that improves real LLM jailbreak robustness-utility tradeoff
For the embedding layer, we set its PEFT hyperparmeters asr=1024,lora alpha=32, and lora dropout=0.1
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Understanding and Improving Continuous Adversarial Training for LLMs via In-context Learning Theory
Continuous adversarial training in the embedding space produces a robust generalization bound for linear transformers that decreases with perturbation radius, tied to singular values of the embedding matrix, and motivates a new regularizer that improves real LLM jailbreak robustness-utility tradeoff