Large Language Model Unlearning via Embedding-Corrupted Prompts
Large language models (LLMs) have advanced to encompass extensive knowledge
across diverse domains. Yet controlling what a large language model should not
know is important for ensuring alignment and thus safe use. However, accurately
and efficiently unlearning knowledge from an LLM remains challenging due to the
potential collateral damage caused by the fuzzy boundary between retention and
forgetting, and the large computational requirements for optimization across
state-of-the-art models with hundreds of billions of parameters. In this work,
we present Embedding-COrrupted (ECO) Prompts, a lightweight unlearning
framework for large language models to address both the challenges of knowledge
entanglement and unlearning efficiency. Instead of relying on the LLM itself to
unlearn, we enforce an unlearned state during inference by employing a prompt
classifier to identify and safeguard prompts to forget. We learn corruptions
added to prompt embeddings via zeroth order optimization toward the unlearning
objective offline and corrupt prompts flagged by the classifier during
inference. We find that these embedding-corrupted prompts not only lead to
desirable outputs that satisfy the unlearning objective but also closely
approximate the output from a model that has never been trained on the data
intended for forgetting. Through extensive experiments on unlearning, we
demonstrate the superiority of our method in achieving promising unlearning at
nearly zero side effects in general domains and domains closely related to the
unlearned ones. Additionally, we highlight the scalability of our method to 100
LLMs, ranging from 0.5B to 236B parameters, incurring no additional cost as the
number of parameters increases.
666a59e2f0d87d9c3b1de24b
Chris Yuhao Liu
claimed_verified
2024-06-13T09:31:07.612Z
658229ef5f6d83438257fce5
/avatars/b4417de9a338e95dc69cc547a46348e8.svg
Chris (Yuhao) Liu
user
chrisliu298
666a59e2f0d87d9c3b1de24c
Yaxuan Wang
admin_assigned
2024-06-13T20:27:12.286Z
6537dbf4ca70ffcd7234a012
/avatars/82c9490e3b36667d90cfb52b925b9c92.svg
Yaxuan Wang
user
Yaxuan11
666a59e2f0d87d9c3b1de24d
Jeffrey Flanigan
666a59e2f0d87d9c3b1de24e
Yang Liu
Large Language Model Unlearning via Embedding-Corrupted Prompts
Large language models (LLMs) have advanced to encompass extensive knowledge
across diverse domains. Yet controlling what a large language model should not
know is important for ensuring alignment and thus safe use. However, accurately
and efficiently unlearning knowledge from an LLM remains challenging due to the
potential collateral damage caused by the fuzzy boundary between retention and
forgetting, and the large computational requirements for optimization across
state-of-the-art models with hundreds of billions of parameters. In this work,
we present Embedding-COrrupted (ECO) Prompts, a lightweight unlearning
framework for large language models to address both the challenges of knowledge
entanglement and unlearning efficiency. Instead of relying on the LLM itself to
unlearn, we enforce an unlearned state during inference by employing a prompt
classifier to identify and safeguard prompts to forget. We learn corruptions
added to prompt embeddings via zeroth order optimization toward the unlearning
objective offline and corrupt prompts flagged by the classifier during
inference. We find that these embedding-corrupted prompts not only lead to
desirable outputs that satisfy the unlearning objective but also closely
approximate the output from a model that has never been trained on the data
intended for forgetting. Through extensive experiments on unlearning, we
demonstrate the superiority of our method in achieving promising unlearning at
nearly zero side effects in general domains and domains closely related to the
unlearned ones. Additionally, we highlight the scalability of our method to 100
LLMs, ranging from 0.5B to 236B parameters, incurring no additional cost as the
number of parameters increases.
A lightweight unlearning framework using embedding-corrupted prompts addresses knowledge entanglement and efficiency challenges in large language models, enabling effective and side-effect-free unlearning.
large language models unlearning prompt classifier embedding-corrupted prompts zeroth order optimization scalable unlearning