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快速修复:通过对手的快速调整来删除后门

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概括
此摘要是机器生成的。

在自然语言处理 (NLP) 模型中,PromptFix提供了对后门的新防御. 这种方法使用对抗性提示调整来中和恶意触发令牌,而不会改变模型参数,从而提高了少量学习场景的安全性.

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科学领域:

  • 人工智能
  • 自然语言处理
  • 机器学习安全性

背景情况:

  • 预训练语言模型 (PLM) 显示出显著的性能,但容易受到后门的攻击,其中特定的触发令牌操纵模型行为.
  • 由于PLM的通用性和高的培训成本,一些精细调整和提示是流行的NLP培训范式.
  • 现有的后门缓解方法通常需要触发器反转和模型重新训练,这可能是低效的.

研究的目的:

  • 推出一个新的NLP模型后门缓解策略PromptFix
  • 解决几次精确调整和提示范式的漏洞.
  • 开发一种保存模型参数的方法,同时有效中和后门触发器.

主要方法:

  • PromptFix使用两组软令牌进行对抗性提示调整:一个用于接近触发器,另一个用于抵消触发器.
  • 该方法避免了明确的触发反转和模型微调,保持原始模型参数的完整性.
  • 敌对优化用于适应性平衡触发器识别和性能维护.

主要成果:

  • 实验证明了PromptFix在NLP模型中的各种后门攻击的有效性.
  • 这种方法即使在域移动的情况下也表现出强的性能,这表明它适用于未知预训练数据的模型.
  • 在没有损害模型的整体性能的情况下成功中和后门触发.

结论:

  • PromptFix提供了一种有效且参数效率高的解决方案,用于在少数镜头设置中缓解NLP模型的后门.
  • 该技术对域移动具有稳定性,使其适用于现实世界提示调节应用程序.
  • 这种对抗性提示调整方法为提升预先训练的语言模型的安全性提供了有希望的方向.