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@@ -155,10 +155,11 @@ We provide more details about the running flow of Gradient Cuff in the paper.
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 <h2 id="demonstration">Demonstration</h2>
-<p>We evaluated Gradient Cuff as well as 4 baselines (Perplexity Filter, SmoothLLM, Erase-and-Check, and Self-Reminder) against 6 
-  different jailbreak attacks~(GCG, AutoDAN, PAIR, TAP, Base64, and LRL) and benign user queries on 2 LLMs (LLaMA-2-7B-Chat and Vicuna-7B-V1.5). 
-  We demonstrate the average refusal rate across these 6 malicious user query datasets as the Average Malicious Refusal Rate and the refusal rate 
-  on benign user queries as the Benign Refusal Rate. The detection performance against individual jailbreak attacks is shown in the below bar chart.
+<p>We evaluated Gradient Cuff as well as 4 baselines (Perplexity Filter, SmoothLLM, Erase-and-Check, and Self-Reminder) 
+  against 6 different jailbreak attacks~(GCG, AutoDAN, PAIR, TAP, Base64, and LRL) and benign user queries on 2 LLMs (LLaMA-2-7B-Chat and 
+  Vicuna-7B-V1.5). We below demonstrate the average refusal rate across these 6 malicious user query datasets as the Average Malicious Refusal 
+  Rate and the refusal rate on benign user queries as the Benign Refusal Rate. The defending performance against different jailbreak types is 
+  shown in the provided bar chart. 
 </p>