Exponentiated gradient descent over relaxed one-hot token encodings finds adversarial suffixes that jailbreak several open-source LLMs with higher success rate and lower runtime than GCG, PGD, and SoftPromptThreats.
An inertial forward-backward algorithm for the minimization of the sum of two nonconvex functions
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abstract
We propose a forward-backward proximal-type algorithm with inertial/memory effects for minimizing the sum of a nonsmooth function with a smooth one in the nonconvex setting. The sequence of iterates generated by the algorithm converges to a critical point of the objective function provided an appropriate regularization of the objective satisfies the Kurdyka-\L{}ojasiewicz inequality, which is for instance fulfilled for semi-algebraic functions. We illustrate the theoretical results by considering two numerical experiments: the first one concerns the ability of recovering the local optimal solutions of nonconvex optimization problems, while the second one refers to the restoration of a noisy blurred image.
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Adversarial Attack on Large Language Models using Exponentiated Gradient Descent
Exponentiated gradient descent over relaxed one-hot token encodings finds adversarial suffixes that jailbreak several open-source LLMs with higher success rate and lower runtime than GCG, PGD, and SoftPromptThreats.