Large language models (LLMs) risk inadvertently memorizing and divulging
sensitive or personally identifiable information (PII) seen in training data,
causing privacy concerns. Current approaches to address this issue involve
costly dataset scrubbing, or model filtering through unlearning and model
editing, which can be bypassed through extraction attacks. We propose REVS, a
novel model editing method for unlearning sensitive information from LLMs. REVS
identifies and modifies a small subset of neurons relevant for each piece of
sensitive information. By projecting these neurons to the vocabulary space
(unembedding), we pinpoint the components driving its generation. We then
compute a model edit based on the pseudo-inverse of the unembedding matrix, and
apply it to de-promote generation of the targeted sensitive data. To adequately
evaluate our method on truly sensitive information, we curate two datasets: an
email dataset inherently memorized by GPT-J, and a synthetic social security
number dataset that we tune the model to memorize. Compared to other
state-of-the-art model editing methods, REVS demonstrates superior performance
in both eliminating sensitive information and robustness to extraction attacks,
while retaining integrity of the underlying model. The code and a demo notebook
are available at this https URL

通过 REVS 方法，我们可以修改大型语言模型（LLMs）中的少量神经元，从而去除敏感信息、抵御提取攻击，并保持模型的完整性。