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@@ -81,9 +81,12 @@ Exploring Refusal Loss Landscapes </title>
 <p>Current transformer-based LLMs will return different responses to the same query due to the randomness of 
   autoregressive sampling-based generation. With this randomness, it is an 
   interesting phenomenon that a malicious user query will sometimes be rejected by the target LLM, but 
-  sometimes be able to bypass the safety guardrail. Based on this observation, we propose a new concept called <strong>Refusal Loss</strong> to represent the probability with which
-  the LLM won't reject the input user query. Since the refusal loss is not computable, we query the target LLM multiple times using the same query and using the sample 
-  mean of the Jailbroken results (1 indicates successful jailbreak, 0 indicates the opposite) to approximate the function value. Using the approximation, we visualize the 2-D landscape of the Refusal Loss below:
+  sometimes be able to bypass the safety guardrail. Based on this observation, we propose a new concept called <strong>Refusal Loss</strong> to 
+  represent the probability with which the LLM won't reject the input user query. By using 1 to denote successful jailbroken and 0 to denote 
+  the opposite, we compute the empirical refusal loss as the sample mean of the jailbroken results returned from the target LLM.
+  <!--Since the refusal loss is not computable, we query the target LLM multiple times using the same query and using the sample 
+  mean of the Jailbroken results (1 indicates successful jailbreak, 0 indicates the opposite) to approximate the function value. -->
+  We visualize the 2-D landscape of the empirical Refusal Loss as below:
 </p>
 
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