  The robustness of large language models (LLMs) against adversarial
manipulations, such as jailbreak attacks, remains a significant challenge. In
this work, we propose an approach that enhances the self-critique capability of
the LLM and further fine-tunes it over sanitized synthetic data. This is done
with the addition of an external critic model that can be merged with the
original, thus bolstering self-critique capabilities and improving the
robustness of the LLMs response to adversarial prompts. Our results demonstrate
that the combination of merging and self-critique can reduce the attack success
rate of adversaries significantly, thus offering a promising defense mechanism
against jailbreak attacks. Code, data and models released at
https://github.com/vicgalle/merging-self-critique-jailbreaks .
