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While large pre-trained models have enabled impressive results on a varietyof downstream tasks, the largest existing models still make errors, and evenaccurate predictions may become outdated over time. Because detecting all suchfailures at training time is impossible, enabling both developers and end usersof such models to correct inaccurate outputs while leaving the model otherwiseintact is desirable. However, the distributed, black-box nature of therepresentations learned by large neural networks makes producing such targetededits difficult. If presented with only a single problematic input and newdesired output, fine-tuning approaches tend to overfit; other editingalgorithms are either computationally infeasible or simply ineffective whenapplied to very large models. To enable easy post-hoc editing at scale, wepropose Model Editor Networks using Gradient Decomposition (MEND), a collectionof small auxiliary editing networks that use a single desired input-output pairto make fast, local edits to a pre-trained model's behavior. MEND learns totransform the gradient obtained by standard fine-tuning, using a low-rankdecomposition of the gradient to make the parameterization of thistransformation tractable. MEND can be trained on a single GPU in less than aday even for 10 billion+ parameter models; once trained MEND enables rapidapplication of new edits to the pre-trained model. Our experiments with T5,GPT, BERT, and BART models show that MEND is the only approach to model editingthat effectively edits the behavior of models with more than 10 billionparameters. Code and data available athttps://sites.google.com/view/mend-editing.

Fast Model Editing at Scale