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@@ -55,15 +55,18 @@ Exploring Refusal Loss Landscapes </title>
     <main id="content" class="main-content" role="main">
       <h2 id="introduction">Introduction</h2>
 
-<p>Neural network calibration is an essential task in deep learning to ensure consistency 
-between the confidence of model prediction and the true correctness likelihood. In this 
-demonstration, we first visualize the idea of neural network calibration on a binary 
-classifier and show model features that represent its calibration. Second, we introduce 
-our proposed framework <strong>Neural Clamping</strong>, which employs a simple joint input-output 
-transformation on a pre-trained classifier. We also provide other calibration approaches
-(e.g., temperature scaling) to compare with Neural Clamping.</p>
-
-<h2 id="what-is-jailbreak">What is Calibration?</h2>
+<p>Large Language Models (LLMs) are becoming a prominent generative AI tool, where the user enters a 
+  query and the LLM generates an answer. To reduce harm and misuse, efforts have been made to align 
+  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 
+  detect jailbreak attempts. In this demonstration, we first introduce the concept of "Jailbreak". Then we present the refusal loss 
+  landscape and based on the characteristics of this landscape to propose the Gradient Cuff. Lastly, we compare it with other jailbreak defense 
+  methods and show the defense performance.
+</p>
+
+<h2 id="what-is-jailbreak">What is Jailbreak?</h2>
 <p>Neural Network Calibration seeks to make model prediction align with its true correctness likelihood.
 A well-calibrated model should provide accurate predictions and reliable confidence when making inferences. On the
 contrary, a poor calibration model would have a wide gap between its accuracy and average confidence level.