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 this https URL.

本研究提出一种被称为 Transformer-Patcher 的神经网络模型，能够通过简单地添加和训练最后一层前馈网络中的少量神经元，连续纠正长序列中的错误，达到了顺序模型编辑（SME）的最优表现，解决了工业环境中部署的模型如何快速准确地修正错误问题。