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@@ -60,7 +60,7 @@ Exploring Refusal Loss Landscapes </title>
   these LLMs to human values using advanced training techniques such as Reinforcement Learning from 
   Human Feedback (RLHF). However, recent studies have highlighted the vulnerability of LLMs to adversarial 
   jailbreak attempts aiming at subverting the embedded safety guardrails. To address this challenge,
- we define and investigate the $$\textbf{Refusal Loss}$$ of LLMs and then propose a method called \textbf{Gradient Cuff} to 
+ we define and investigate the <strong>Refusal Loss</strong> of LLMs and then propose a method called <strong>Gradient Cuff</strong> to 
   detect jailbreak attempts. In this demonstration, we first introduce the concept of "Jailbreak". Then we present the refusal loss 
   landscape and propose the Gradient Cuff based on the characteristics of this landscape. Lastly, we compare Gradient Cuff with other jailbreak defense 
   methods and show the defense performance.