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Deployed language models decay over time due to shifting inputs, changinguser needs, or emergent world-knowledge gaps. When such problems areidentified, we want to make targeted edits while avoiding expensive retraining.However, current model editors, which modify such behaviors of pre-trainedmodels, degrade model performance quickly across multiple, sequential edits. Wepropose GRACE, a lifelong model editing method, which implements spot-fixes onstreaming errors of a deployed model, ensuring minimal impact on unrelatedinputs. GRACE writes new mappings into a pre-trained model's latent space,creating a discrete, local codebook of edits without altering model weights.This is the first method enabling thousands of sequential edits using onlystreaming errors. Our experiments on T5, BERT, and GPT models show GRACE'sstate-of-the-art performance in making and retaining edits, while generalizingto unseen inputs. Our code is available athttps://www.github.com/thartvigsen/grace}.

Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors