title: Merging Improves Self-Critique Against Jailbreak Attacks
summary: 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 .
paper_authors._id: 666944f7208370bd94f9e559
paper_authors.name: Victor Gallego
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-06-12T06:50:57.153Z
paper_authors._id: 5fad8602b8423e1d80b8a965
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/5fad8602b8423e1d80b8a965/tRqTwcZmrGka8c1vFq2wX.jpeg
paper_authors.fullname: Victor Gallego
paper_authors.type: user
paper_authors.user: vicgalle
paper_title: Merging Improves Self-Critique Against Jailbreak Attacks
paper_summary: 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 .
paper_ai_summary: 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.
paper_ai_keywords: large language models adversarial manipulations jailbreak attacks self-critique capability sanitized synthetic data external critic model merging robustness adversarial prompts