  Large Transformer-based Pretrained Language Models (PLMs) dominate almost all
Natural Language Processing (NLP) tasks. Nevertheless, they still make mistakes
from time to time. For a model deployed in an industrial environment, fixing
these mistakes quickly and robustly is vital to improve user experiences.
Previous works formalize such problems as Model Editing (ME) and mostly focus
on fixing one mistake. However, the one-mistake-fixing scenario is not an
accurate abstraction of the real-world challenge. In the deployment of AI
services, there are ever-emerging mistakes, and the same mistake may recur if
not corrected in time. Thus a preferable solution is to rectify the mistakes as
soon as they appear nonstop. Therefore, we extend the existing ME into
Sequential Model Editing (SME) to help develop more practical editing methods.
Our study shows that most current ME methods could yield unsatisfying results
in this scenario. We then introduce Transformer-Patcher, a novel model editor
that can shift the behavior of transformer-based models by simply adding and
training a few neurons in the last Feed-Forward Network layer. Experimental
results on both classification and generation tasks show that
Transformer-Patcher can successively correct up to thousands of errors
(Reliability) and generalize to their equivalent inputs (Generality) while
retaining the model's accuracy on irrelevant inputs (Locality). Our method
outperforms previous fine-tuning and HyperNetwork-based methods and achieves
state-of-the-art performance for Sequential Model Editing (SME). The code is
available at https://github.com/ZeroYuHuang/Transformer-Patcher.
