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@@ -93,9 +93,9 @@ Exploring Refusal Loss Landscapes </title>
 <p>
   From the above plot, we find that the loss landscape is more precipitous for malicious queries than for benign queries, which implies that 
   the <strong>Refusal Loss</strong> tends to have a large gradient norm if the input represents a malicious query. This observation motivates our proposal of using 
-  the gradient norm of <strong>Refusal Loss</strong> to detect jailbreak attempts that pass the initial filtering of rejecting the input query when the function value is under 0.5 (this is a naive detector bacause the Refusal Loss can be regarded the probability that the LLM won't reject the user query).
+  the gradient norm of <strong>Refusal Loss</strong> to detect jailbreak attempts that pass the initial filtering of rejecting the input query when the function value is under 0.5 (this is a naive detector bacause the Refusal Loss can be regarded as the probability that the LLM won't reject the user query).
   Below we present the definition of the <strong>Refusal Loss</strong> and the approximation of it's function value and gradient. 
-  See more details about the concept, approximation, gradient estimation and landscape drawing of it in our paper. 
+  See more details about the concept, approximation, gradient estimation and landscape drawing techniques of it in our paper. 
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