title: Safeguard Fine-Tuned LLMs Through Pre- and Post-Tuning Model Merging
summary: Fine-tuning large language models (LLMs) for downstream tasks is a widely
  adopted approach, but it often leads to safety degradation in safety-aligned
  LLMs. Currently, many solutions address this issue by incorporating additional
  safety data, which can be impractical in many cases. In this paper, we address
  the question: How can we improve downstream task performance while preserving
  safety in LLMs without relying on additional safety data? We propose a simple
  and effective method that maintains the inherent safety of LLMs while enhancing
  their downstream task performance: merging the weights of pre- and
  post-fine-tuned safety-aligned models. Experimental results across various
  downstream tasks, models, and merging methods demonstrate that this approach
  effectively mitigates safety degradation while improving downstream task
  performance, offering a practical solution for adapting safety-aligned LLMs.
paper_authors._id: 67727ca4986fbffa7a2208d4
paper_authors.name: Hua Farn
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-12-30T10:57:42.126Z
paper_authors._id: 641a9cb1f9dd6391a2463477
paper_authors.avatarUrl: /avatars/73caf5389f88c7356f02ee9f73146faa.svg
paper_authors.fullname: Farn Hua
paper_authors.type: user
paper_authors.user: farnhua
paper_authors._id: 67727ca4986fbffa7a2208d5
paper_authors.name: Hsuan Su
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-30T19:29:21.989Z
paper_authors._id: 608abf1272b50b02c4b02865
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1619708309549-608abf1272b50b02c4b02865.jpeg
paper_authors.fullname: Hsuan Su
paper_authors.type: user
paper_authors.user: jacksukk
paper_authors._id: 67727ca4986fbffa7a2208d6
paper_authors.name: Shachi H Kumar
paper_authors._id: 67727ca4986fbffa7a2208d7
paper_authors.name: Saurav Sahay
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-30T20:52:51.579Z
paper_authors._id: 626c5d06f451470f86252659
paper_authors.avatarUrl: /avatars/41078e5f99a7660b979c8cf7a63d71c6.svg
paper_authors.fullname: Saurav Sahay
paper_authors.type: user
paper_authors.user: sauravsahay
paper_authors._id: 67727ca4986fbffa7a2208d8
paper_authors.name: Shang-Tse Chen
paper_authors._id: 67727ca4986fbffa7a2208d9
paper_authors.name: Hung-yi Lee
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-30T20:53:02.019Z
paper_authors._id: 629b6eb3ad498ab19282ae6f
paper_authors.avatarUrl: /avatars/adade8c512122120b06e5c50724a178d.svg
paper_authors.fullname: Hung-yi Lee
paper_authors.type: user
paper_authors.user: hungyilee
paper_title: Safeguard Fine-Tuned LLMs Through Pre- and Post-Tuning Model Merging
paper_summary: Fine-tuning large language models (LLMs) for downstream tasks is a widely
  adopted approach, but it often leads to safety degradation in safety-aligned
  LLMs. Currently, many solutions address this issue by incorporating additional
  safety data, which can be impractical in many cases. In this paper, we address
  the question: How can we improve downstream task performance while preserving
  safety in LLMs without relying on additional safety data? We propose a simple
  and effective method that maintains the inherent safety of LLMs while enhancing
  their downstream task performance: merging the weights of pre- and
  post-fine-tuned safety-aligned models. Experimental results across various
  downstream tasks, models, and merging methods demonstrate that this approach
  effectively mitigates safety degradation while improving downstream task
  performance, offering a practical solution for adapting safety-aligned LLMs.
paper_ai_summary: Merging weights of pre- and post-fine-tuned safety-aligned language models improves downstream task performance while preserving safety without additional safety data.
paper_ai_keywords: large language models fine-tuning safety degradation safety-aligned models merging weights downstream tasks