While displaying impressive generation capabilities across many tasks, Large Language Models (LLMs) still struggle with crucial issues of privacy violation and unwanted exposure of sensitive data. This raises an essential question: how should we prevent such undesired behavior of LLMs while maintaining their strong generation and natural language understanding (NLU) capabilities? In this work, we introduce a novel approach termed deliberate imagination in the context of LLM unlearning. Instead of trying to forget memorized data, we employ a self-distillation framework, guiding LLMs to deliberately imagine alternative scenarios. As demonstrated in a wide range of experiments, the proposed method not only effectively unlearns targeted text but also preserves the LLMs' capabilities in open-ended generation tasks as well as in NLU tasks. Our results demonstrate the usefulness of this approach across different models and sizes, and also with parameter-efficient fine-tuning, offering a novel pathway to addressing the challenges with private and sensitive data in LLM applications.

翻译：尽管大语言模型（LLMs）在多项任务中展现出强大的生成能力，但其仍面临隐私泄露与敏感数据非预期暴露等关键问题。这引发了一个核心议题：如何在保持LLMs强大生成能力与自然语言理解能力的同时，有效抑制其不当行为？本文提出了一种名为"有意想象"的创新方法，应用于LLMs的遗忘学习场景。我们摒弃传统强制遗忘记忆数据的思路，转而采用自蒸馏框架，引导LLMs主动想象替代性场景。大量实验表明，该方法不仅能有效遗忘目标文本，还能保留LLMs在开放式生成任务及自然语言理解任务中的能力。实验结果验证了该方法在不同模型架构、不同参数量级及参数高效微调场景中的有效性，为应对LLM应用中私有数据与敏感数据挑战提供了新路径。