STAIR: Improving Safety Alignment with Introspective Reasoning
- URL: http://arxiv.org/abs/2502.02384v1
- Date: Tue, 04 Feb 2025 15:02:55 GMT
- Title: STAIR: Improving Safety Alignment with Introspective Reasoning
- Authors: Yichi Zhang, Siyuan Zhang, Yao Huang, Zeyu Xia, Zhengwei Fang, Xiao Yang, Ranjie Duan, Dong Yan, Yinpeng Dong, Jun Zhu,
- Abstract summary: We propose STAIR, a framework that integrates SafeTy Alignment with Itrospective Reasoning.
We show that STAIR effectively mitigates harmful outputs while better preserving helpfulness, compared to instinctive alignment strategies.
With test-time scaling, STAIR achieves a safety performance comparable to Claude-3.5 against popular jailbreak attacks.
- Score: 44.780098674618614
- License:
- Abstract: Ensuring the safety and harmlessness of Large Language Models (LLMs) has become equally critical as their performance in applications. However, existing safety alignment methods typically suffer from safety-performance trade-offs and the susceptibility to jailbreak attacks, primarily due to their reliance on direct refusals for malicious queries. In this paper, we propose STAIR, a novel framework that integrates SafeTy Alignment with Itrospective Reasoning. We enable LLMs to identify safety risks through step-by-step analysis by self-improving chain-of-thought (CoT) reasoning with safety awareness. STAIR first equips the model with a structured reasoning capability and then advances safety alignment via iterative preference optimization on step-level reasoning data generated using our newly proposed Safety-Informed Monte Carlo Tree Search (SI-MCTS). We further train a process reward model on this data to guide test-time searches for improved responses. Extensive experiments show that STAIR effectively mitigates harmful outputs while better preserving helpfulness, compared to instinctive alignment strategies. With test-time scaling, STAIR achieves a safety performance comparable to Claude-3.5 against popular jailbreak attacks. Relevant resources in this work are available at https://github.com/thu-ml/STAIR.
Related papers
- MetaSC: Test-Time Safety Specification Optimization for Language Models [0.6526824510982799]
We propose a novel dynamic safety framework that optimize language model (LM) safety reasoning at inference time without modifying model weights.
We leverage a meta-critique mechanism that iteratively updates safety prompts-termed specifications to drive the critique and revision process adaptively.
arXiv Detail & Related papers (2025-02-11T22:06:25Z) - Vulnerability Mitigation for Safety-Aligned Language Models via Debiasing [12.986006070964772]
Safety alignment is an essential research topic for real-world AI applications.
Our study first identified the difficulty of eliminating such vulnerabilities without sacrificing the model's helpfulness.
Our method could enhance the model's helpfulness while maintaining safety, thus improving the trade-off-front.
arXiv Detail & Related papers (2025-02-04T09:31:54Z) - Internal Activation as the Polar Star for Steering Unsafe LLM Behavior [50.463399903987245]
We introduce SafeSwitch, a framework that dynamically regulates unsafe outputs by monitoring and utilizing the model's internal states.
Our empirical results show that SafeSwitch reduces harmful outputs by over 80% on safety benchmarks while maintaining strong utility.
arXiv Detail & Related papers (2025-02-03T04:23:33Z) - Separate the Wheat from the Chaff: A Post-Hoc Approach to Safety Re-Alignment for Fine-Tuned Language Models [30.93821289892195]
We propose IRR (Identify, Remove, and Recalibrate for Safety Realignment) that performs safety realignment for LLMs.
The core of IRR is to identify and remove unsafe delta parameters from the fine-tuned models, while recalibrating the retained ones.
Our results demonstrate that IRR significantly enhances the safety performance of fine-tuned models on safety benchmarks, such as harmful queries and jailbreak attacks.
arXiv Detail & Related papers (2024-12-15T03:58:38Z) - SCANS: 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) - The Art of Defending: A Systematic Evaluation and Analysis of LLM
Defense Strategies on Safety and Over-Defensiveness [56.174255970895466]
Large Language Models (LLMs) play an increasingly pivotal role in natural language processing applications.
This paper presents Safety and Over-Defensiveness Evaluation (SODE) benchmark.
arXiv Detail & Related papers (2023-12-30T17:37:06Z)
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.