Related papers: Unlearn What You Want to Forget: Efficient Unlearning for LLMs

Unlearn What You Want to Forget: Efficient Unlearning for LLMs

URL: http://arxiv.org/abs/2310.20150v1
Date: Tue, 31 Oct 2023 03:35:59 GMT
Title: Unlearn What You Want to Forget: Efficient Unlearning for LLMs
Authors: Jiaao Chen, Diyi Yang
Abstract summary: Large language models (LLMs) have achieved significant progress from pre-training on and memorizing a wide range of textual data. This process might suffer from privacy issues and violations of data protection regulations. We propose an efficient unlearning framework that could efficiently update LLMs without having to retrain the whole model after data removals.
Score: 92.51670143929056
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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