Controllable Safety Alignment: Inference-Time Adaptation to Diverse Safety Requirements

• URL: http://arxiv.org/abs/2410.08968v1
• Date: Fri, 11 Oct 2024 16:38:01 GMT
• Title: Controllable Safety Alignment: Inference-Time Adaptation to Diverse Safety Requirements
• Authors: Jingyu Zhang, Ahmed Elgohary, Ahmed Magooda, Daniel Khashabi, Benjamin Van Durme,
• Abstract summary: The current paradigm for safety alignment of large language models (LLMs) follows a one-size-fits-all approach. We propose Controllable Safety Alignment (CoSA), a framework designed to adapt models to diverse safety requirements without re-training.
• Score: 46.79887158348167
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Progressive Safeguards for Safe and Model-Agnostic Reinforcement Learning [5.593642806259113]
  We model a meta-learning process where each task is synchronized with a safeguard that monitors safety and provides a reward signal to the agent. The design of the safeguard is manual but it is high-level and model-agnostic, which gives rise to an end-to-end safe learning approach. We evaluate our framework in a Minecraft-inspired Gridworld, a VizDoom game environment, and an LLM fine-tuning application.
  arXiv  Detail & Related papers  (2024-10-31T16:28:33Z)
• Superficial Safety Alignment Hypothesis [8.297367440457508]
  We propose the Superficial Safety Alignment Hypothesis (SSAH), which posits that safety alignment should teach an otherwise unsafe model to choose the correct reasoning direction. We identify four types of attribute-critical components in safety-aligned large language models (LLMs) Our findings show that freezing certain safety-critical components 7.5% during fine-tuning allows the model to retain its safety attributes while adapting to new tasks.
  arXiv  Detail & Related papers  (2024-10-07T19:53:35Z)
• Nothing in Excess: Mitigating the Exaggerated Safety for LLMs via Safety-Conscious Activation Steering [56.92068213969036]
  Safety alignment is indispensable for Large language models (LLMs) to defend threats from malicious instructions. Recent researches reveal safety-aligned LLMs prone to reject benign queries due to the exaggerated safety issue. We propose a Safety-Conscious Activation Steering (SCANS) method to mitigate the exaggerated safety concerns.
  arXiv  Detail & Related papers  (2024-08-21T10:01:34Z)
• What Makes and Breaks Safety Fine-tuning? A Mechanistic Study [64.9691741899956]
  Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. We design a synthetic data generation framework that captures salient aspects of an unsafe input. Using this, we investigate three well-known safety fine-tuning methods.
  arXiv  Detail & Related papers  (2024-07-14T16:12:57Z)
• Refuse Whenever You Feel Unsafe: Improving Safety in LLMs via Decoupled Refusal Training [67.30423823744506]
  This study addresses a critical gap in safety tuning practices for Large Language Models (LLMs) We introduce a novel approach, Decoupled Refusal Training (DeRTa), designed to empower LLMs to refuse compliance to harmful prompts at any response position. DeRTa incorporates two novel components: (1) Maximum Likelihood Estimation with Harmful Response Prefix, which trains models to recognize and avoid unsafe content by appending a segment of harmful response to the beginning of a safe response, and (2) Reinforced Transition Optimization (RTO), which equips models with the ability to transition from potential harm to safety refusal consistently throughout the harmful
  arXiv  Detail & Related papers  (2024-07-12T09:36:33Z)
• SafeInfer: Context Adaptive Decoding Time Safety Alignment for Large Language Models [5.6874111521946356]
  Safety-aligned language models often exhibit fragile and imbalanced safety mechanisms. We propose SafeInfer, a context-adaptive, decoding-time safety alignment strategy. HarmEval is a novel benchmark for extensive safety evaluations.
  arXiv  Detail & Related papers  (2024-06-18T05:03:23Z)
• Safety Arithmetic: A Framework for Test-time Safety Alignment of Language Models by Steering Parameters and Activations [19.132597762214722]
  Current alignment methods struggle with dynamic user intentions and complex objectives. We propose Safety Arithmetic, a training-free framework enhancing safety across different scenarios. Our experiments show that Safety Arithmetic significantly improves safety measures, reduces over-safety, and maintains model utility.
  arXiv  Detail & Related papers  (2024-06-17T17:48:13Z)
• Navigating the Safety Landscape: Measuring Risks in Finetuning Large Language Models [65.06446825020578]
  Safety alignment is crucial to ensure that large language models (LLMs) behave in ways that align with human preferences and prevent harmful actions during inference. We aim to measure the risks in finetuning LLMs through navigating the LLM safety landscape.
  arXiv  Detail & Related papers  (2024-05-27T17:31:56Z)
• Towards Comprehensive and Efficient Post Safety Alignment of Large Language Models via Safety Patching [77.36097118561057]
  textscSafePatching is a novel framework for comprehensive and efficient PSA. textscSafePatching achieves a more comprehensive and efficient PSA than baseline methods.
  arXiv  Detail & Related papers  (2024-05-22T16:51:07Z)
• Safe Reinforcement Learning with Learned Non-Markovian Safety Constraints [15.904640266226023]
  We design a safety model that performs credit assignment to assess contributions of partial state-action trajectories on safety. We derive an effective algorithm for optimizing a safe policy using the learned safety model. We devise a method to dynamically adapt the tradeoff coefficient between safety reward and safety compliance.
  arXiv  Detail & Related papers  (2024-05-05T17:27:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.