3 citations · 5 across the 28 of their papers we have counts for
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cs.LG2024★ 1 cited
New Paradigm of Adversarial Training: Releasing Accuracy-Robustness Trade-Off via Dummy Class
Yanyun Wang, Li Liu, Zi Liang +4
Adversarial Training (AT) is one of the most effective methods to enhance the robustness of Deep Neural Networks (DNNs). However, existing AT methods suffer from an inherent accura…
cs.CR2024
"Yes, My LoRD." Guiding Language Model Extraction with Locality Reinforced Distillation
Zi Liang, Qingqing Ye, Yanyun Wang +5
Model extraction attacks (MEAs) on large language models (LLMs) have received increasing attention in recent research. However, existing attack methods typically adapt the extracti…
cs.CL2024★ 1 cited
Why Are My Prompts Leaked? Unraveling Prompt Extraction Threats in Customized Large Language Models
Zi Liang, Haibo Hu, Qingqing Ye +2
The drastic increase of large language models' (LLMs) parameters has led to a new research direction of fine-tuning-free downstream customization by prompts, i.e., task description…