title: Controllable Safety Alignment: Inference-Time Adaptation to Diverse
    Safety Requirements
summary: The current paradigm for safety alignment of large language models (LLMs)
  follows a one-size-fits-all approach: the model refuses to interact with any
  content deemed unsafe by the model provider. This approach lacks flexibility in
  the face of varying social norms across cultures and regions. In addition,
  users may have diverse safety needs, making a model with static safety
  standards too restrictive to be useful, as well as too costly to be re-aligned.
    We propose Controllable Safety Alignment (CoSA), a framework designed to
  adapt models to diverse safety requirements without re-training. Instead of
  aligning a fixed model, we align models to follow safety configs -- free-form
  natural language descriptions of the desired safety behaviors -- that are
  provided as part of the system prompt. To adjust model safety behavior,
  authorized users only need to modify such safety configs at inference time. To
  enable that, we propose CoSAlign, a data-centric method for aligning LLMs to
  easily adapt to diverse safety configs. Furthermore, we devise a novel
  controllability evaluation protocol that considers both helpfulness and
  configured safety, summarizing them into CoSA-Score, and construct CoSApien, a
  human-authored benchmark that consists of real-world LLM use cases with diverse
  safety requirements and corresponding evaluation prompts.
    We show that CoSAlign leads to substantial gains of controllability over
  strong baselines including in-context alignment. Our framework encourages
  better representation and adaptation to pluralistic human values in LLMs, and
  thereby increasing their practicality.
paper_authors._id: 67110e21e15dea1149bdfc6b
paper_authors.name: Jingyu Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-18T08:14:06.644Z
paper_authors._id: 62fb40b59af1d16bc0ac60f4
paper_authors.avatarUrl: /avatars/03ff66a419db8f2bc8e89a3b47aaaeac.svg
paper_authors.fullname: Jack Zhang
paper_authors.type: user
paper_authors.user: jackzhang
paper_authors._id: 67110e21e15dea1149bdfc6c
paper_authors.name: Ahmed Elgohary
paper_authors._id: 67110e21e15dea1149bdfc6d
paper_authors.name: Ahmed Magooda
paper_authors._id: 67110e21e15dea1149bdfc6e
paper_authors.name: Daniel Khashabi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-27T15:50:32.022Z
paper_authors._id: 5f6540c65e78cc6b0ed3199d
paper_authors.avatarUrl: /avatars/0280d4df417855965a0964d22766c012.svg
paper_authors.fullname: Daniel Khashabi
paper_authors.type: user
paper_authors.user: danyaljj
paper_authors._id: 67110e21e15dea1149bdfc6f
paper_authors.name: Benjamin Van Durme
paper_title: Controllable Safety Alignment: Inference-Time Adaptation to Diverse
    Safety Requirements
paper_summary: The current paradigm for safety alignment of large language models (LLMs)
  follows a one-size-fits-all approach: the model refuses to interact with any
  content deemed unsafe by the model provider. This approach lacks flexibility in
  the face of varying social norms across cultures and regions. In addition,
  users may have diverse safety needs, making a model with static safety
  standards too restrictive to be useful, as well as too costly to be re-aligned.
    We propose Controllable Safety Alignment (CoSA), a framework designed to
  adapt models to diverse safety requirements without re-training. Instead of
  aligning a fixed model, we align models to follow safety configs -- free-form
  natural language descriptions of the desired safety behaviors -- that are
  provided as part of the system prompt. To adjust model safety behavior,
  authorized users only need to modify such safety configs at inference time. To
  enable that, we propose CoSAlign, a data-centric method for aligning LLMs to
  easily adapt to diverse safety configs. Furthermore, we devise a novel
  controllability evaluation protocol that considers both helpfulness and
  configured safety, summarizing them into CoSA-Score, and construct CoSApien, a
  human-authored benchmark that consists of real-world LLM use cases with diverse
  safety requirements and corresponding evaluation prompts.
    We show that CoSAlign leads to substantial gains of controllability over
  strong baselines including in-context alignment. Our framework encourages
  better representation and adaptation to pluralistic human values in LLMs, and
  thereby increasing their practicality.
paper_ai_summary: A new framework, Controllable Safety Alignment (CoSA), enhances LLMs' flexibility and adaptability to varying safety requirements without the need for retraining through dynamic configuration adjustments at inference time.
paper_ai_keywords: Controllable Safety Alignment (CoSA) safety configs CoSAlign in-context alignment CoSA-Score CoSApien large language models (LLMs) safety alignment