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@@ -81,7 +81,7 @@ Exploring Refusal Loss Landscapes </title>
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  <p>Current transformer-based LLMs will return different responses to the same query due to the randomness of
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  autoregressive sampling-based generation. With this randomness, it is an
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  interesting phenomenon that a malicious user query will sometimes be rejected by the target LLM, but
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- sometimes be able to bypass the safety guardrail. Based on this observation, for a given LLM <p>$T_\theta$</p>
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  parameterized with $\theta$, we define the refusal loss function $\phi_\theta(x)$ for a given input user query $x$ as below:
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  </p>
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  <p>Current transformer-based LLMs will return different responses to the same query due to the randomness of
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  autoregressive sampling-based generation. With this randomness, it is an
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  interesting phenomenon that a malicious user query will sometimes be rejected by the target LLM, but
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+ sometimes be able to bypass the safety guardrail. Based on this observation, for a given LLM T_\theta
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  parameterized with $\theta$, we define the refusal loss function $\phi_\theta(x)$ for a given input user query $x$ as below:
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  </p>
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