SafeInfer: Context Adaptive Decoding Time Safety Alignment for Large Language Models

• URL: http://arxiv.org/abs/2406.12274v1
• Date: Tue, 18 Jun 2024 05:03:23 GMT
• Title: SafeInfer: Context Adaptive Decoding Time Safety Alignment for Large Language Models
• Authors: Somnath Banerjee, Soham Tripathy, Sayan Layek, Shanu Kumar, Animesh Mukherjee, Rima Hazra,
• Abstract summary: 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.
• Score: 5.6874111521946356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Safety-aligned language models often exhibit fragile and imbalanced safety mechanisms, increasing the likelihood of generating unsafe content. In addition, incorporating new knowledge through editing techniques to language models can further compromise safety. To address these issues, we propose SafeInfer, a context-adaptive, decoding-time safety alignment strategy for generating safe responses to user queries. SafeInfer comprises two phases: the safety amplification phase, which employs safe demonstration examples to adjust the model's hidden states and increase the likelihood of safer outputs, and the safety-guided decoding phase, which influences token selection based on safety-optimized distributions, ensuring the generated content complies with ethical guidelines. Further, we present HarmEval, a novel benchmark for extensive safety evaluations, designed to address potential misuse scenarios in accordance with the policies of leading AI tech giants.

Related papers

• Controllable Safety Alignment: Inference-Time Adaptation to Diverse Safety Requirements [46.79887158348167]
  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.
  arXiv  Detail & Related papers  (2024-10-11T16:38:01Z)
• Multimodal Situational Safety [73.63981779844916]
  We present the first evaluation and analysis of a novel safety challenge termed Multimodal Situational Safety. For an MLLM to respond safely, whether through language or action, it often needs to assess the safety implications of a language query within its corresponding visual context. We develop the Multimodal Situational Safety benchmark (MSSBench) to assess the situational safety performance of current MLLMs.
  arXiv  Detail & Related papers  (2024-10-08T16:16:07Z)
• 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)
• Safe-Embed: Unveiling the Safety-Critical Knowledge of Sentence Encoders [5.070104802923903]
  Unsafe prompts pose a significant threat to Large Language Models (LLMs) This paper investigates the potential of sentence encoders to distinguish safe from unsafe prompts. We introduce new pairwise datasets and the Categorical Purity metric to measure this capability.
  arXiv  Detail & Related papers  (2024-07-09T13:35:54Z)
• SafeAligner: Safety Alignment against Jailbreak Attacks via Response Disparity Guidance [48.80398992974831]
  SafeAligner is a methodology implemented at the decoding stage to fortify defenses against jailbreak attacks. We develop two specialized models: the Sentinel Model, which is trained to foster safety, and the Intruder Model, designed to generate riskier responses. We show that SafeAligner can increase the likelihood of beneficial tokens, while reducing the occurrence of harmful ones.
  arXiv  Detail & Related papers  (2024-06-26T07:15:44Z)
• 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)
• 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.