裁剪不安全的票据：一种资源高效的更安全且更稳健的大语言模型框架
Pruning Unsafe Tickets: A Resource-Efficient Framework for Safer and More Robust LLMs
AI总结：
本文提出一种资源高效的剪枝框架，通过识别并移除与不安全行为相关的参数，减少不安全生成并提高对抗鲁棒性，同时保持模型性能。
摘要：
机器学习模型越来越多地应用于现实世界，但即使对齐的模型如Mistral和LLaVA仍表现出继承自预训练的不安全行为。当前对齐方法如SFT和RLHF主要鼓励模型生成首选响应，但并未显式移除触发有害输出的不安全子网络。本文介绍了一种资源高效的剪枝框架，直接识别并移除与不安全行为相关的参数，同时保持模型效用。我们的方法采用无梯度的归因机制，仅需少量GPU资源，并适用于各种架构和量化变体。在ML模型上的实证评估显示，不安全生成显著减少，对抗鲁棒性提高，同时性能损失极小。从彩票假说的角度，我们的结果表明ML模型包含负责有害行为的
英文摘要：
Machine learning models are increasingly deployed in real-world applications, but even aligned models such as Mistral and LLaVA still exhibit unsafe behaviors inherited from pre-training. Current alignment methods like SFT and RLHF primarily encourage models to generate preferred responses, but do not explicitly remove the unsafe subnetworks that trigger harmful outputs. In this work, we introduce a resource-efficient pruning framework that directly identifies and removes parameters associated with unsafe behaviors while preserving model utility. Our method employs a gradient-free attribution mechanism, requiring only modest GPU resources, and generalizes across architectures and quantized variants. Empirical evaluations on ML models show substantial reductions in unsafe generations and improved robustness against jailbreak attacks, with minimal utility loss. From the perspective of the Lottery Ticket Hypothesis, our results suggest that ML models contain "unsafe tickets" responsible for harmful behaviors, and pruning reveals "safety tickets" that maintain performance while aligning outputs. This provides a lightweight, post-hoc alignment strategy suitable for deployment in resource-constrained settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
