Merging Improves Self-Critique Against Jailbreak Attacks
<|reference_start|>Merging Improves Self-Critique Against Jailbreak Attacks: 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 .<|reference_end|>
@article{gallego2024merging,
    title={Merging Improves Self-Critique Against Jailbreak Attacks},
    author={Victor Gallego},
    journal={arXiv preprint arXiv:2406.07188},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.07188},
    primaryClass={cs.CL cs.AI}
}