Large language models (LLMs) have achieved significant progress from pre-training on and memorizing a wide range of textual data, however, this process might suffer from privacy issues and violations of data protection regulations. As a result, the ability to easily remove data related to individual users from such models while not deteriorating their predictive quality after the removal becomes increasingly important. To address these issues, in this work, we propose an efficient unlearning framework that could efficiently update LLMs without having to retrain the whole model after data removals, by introducing lightweight unlearning layers learned with a selective teacher-student objective into the transformers. In addition, we introduce a fusion mechanism to effectively combine different unlearning layers that learns to forget different sets of data to handle a sequence of forgetting operations. Experiments on classification and generation tasks demonstrate the effectiveness of our proposed methods compared to the state-of-the-art baselines.

提出了一种高效的取消学习框架，通过引入轻量级的取消学习层并与transformers结合，可以在不对整个模型重新训练的情况下有效地更新大型语言模型，以解决用户数据隐私与数据保护法规的问题。实验证明，与现有技术相比，我们提出的方法在分类和生成任务上的有效性得到了验证。

遗忘您想遗忘的内容：针对LLMs的高效遗忘方法