标题： 基于多维攻击与防御的大型模型生成安全性增强方法 显示英文标题

标题： A Method for Enhancing the Safety of Large Model Generation Based on Multi-dimensional Attack and Defense

摘要： 目前，大型模型在面对复杂攻击指令时容易生成有害内容，显著降低了其防御能力。 为解决这个问题，本文提出了一种基于构建与多维攻击防御对齐的数据的方法，以增强大型模型的生成安全性。 我们方法的核心在于通过创新性地增加攻击指令维度的多样性以及生成安全响应的准确性，来提高大型模型的安全对齐学习效果。 为了验证我们方法的有效性，除了现有的安全评估基准外，我们还设计了新的安全评估基准，并使用Llama3.2作为基线模型进行了对比实验。 最终的实验结果表明，我们的方法可以在复杂指令攻击下显著提升大型模型的生成安全性，同时保持并增强模型的通用能力。 显示英文摘要

摘要： Currently, large models are prone to generating harmful content when faced with complex attack instructions, significantly reducing their defensive capabilities. To address this issue, this paper proposes a method based on constructing data aligned with multi-dimensional attack defense to enhance the generative security of large models. The core of our method lies in improving the effectiveness of safe alignment learning for large models by innova-tively increasing the diversity of attack instruction dimensions and the accuracy of generat-ing safe responses. To validate the effectiveness of our method, beyond existing security evaluation benchmarks, we additionally designed new security evaluation benchmarks and conducted comparative experiments using Llama3.2 as the baseline model. The final ex-perimental results demonstrate that our method can significantly improve the generative security of large models under complex instructional attacks, while also maintaining and enhancing the models' general capabilities.