Sequential fine-tuning across related bias-detection tasks tends to raise adversarial attack success rates, but the size-resilience pattern the paper highlights is not borne out by its own data.
Towards assurance of llm adversarial robustness using ontology-driven argumentation
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On Adversarial Robustness of Language Models in Transfer Learning
Sequential fine-tuning across related bias-detection tasks tends to raise adversarial attack success rates, but the size-resilience pattern the paper highlights is not borne out by its own data.