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 .
666944f7208370bd94f9e559
Victor Gallego
extracted_confirmed
2024-06-12T06:50:57.153Z
5fad8602b8423e1d80b8a965
https://cdn-avatars.huggingface.co/v1/production/uploads/5fad8602b8423e1d80b8a965/tRqTwcZmrGka8c1vFq2wX.jpeg
Victor Gallego
user
vicgalle
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 .
An approach enhances LLM robustness against adversarial attacks by combining self-critique with an external critic model, leading to significant reduction in attack success rates.
large language models adversarial manipulations jailbreak attacks self-critique capability sanitized synthetic data external critic model merging robustness adversarial prompts