DualOptim+: 联合与解耦优化器状态的桥梁以提升大语言模型中的机器反遗忘
DualOptim+: Bridging Shared and Decoupled Optimizer States for Better Machine Unlearning in Large Language Models
AI总结：
本文提出DualOptim+，一种改进大语言模型中机器反遗忘的新优化框架，通过引入基础状态和delta状态，有效平衡遗忘与保留目标，同时提出8位量化变体以减少内存开销，实验表明其在多个任务中均表现出色。
摘要：
我们提出DualOptim+，一种新的优化框架，用于改进大语言模型中的机器反遗忘。它引入了一个基础状态来捕捉遗忘和保留目标共享的表示，以及delta状态来保存目标特定的残差。这种架构允许优化器根据遗忘和保留梯度之间的方向冲突，自适应地连接共享和解耦状态。我们进一步引入DualOptim+ 8bit，一种量化变体，能够在不牺牲性能的情况下减少内存开销。在虚构和现实世界的反遗忘、安全对齐和多任务学习任务中进行的广泛实验表明，DualOptim+ 一致地在不同目标之间实现了更优的权衡。代码可在https://github.com/CityU-MLO/DualOptimPlus上获得。
英文摘要：
We propose DualOptim+, a novel optimization framework for improving machine unlearning in large language models. It introduces a base state to capture common representations shared by forgetting and retaining objectives and delta states to preserve objective-specific residuals. This architecture allows the optimizer to adaptively bridge shared and decoupled states based on the directional conflict between forgetting and retaining gradients. We further introduce DualOptim+ 8bit, a quantized variant that reduces memory overhead without compromising performance. Extensive experiments across fictitious and real-world unlearning, safety alignment, and multi-task learning tasks demonstrate that DualOptim+ consistently achieves a superior trade-off between different objectives. Codes are available at https://github.com/CityU-MLO/DualOptimPlus.
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。
