  Deep neural networks are becoming increasingly pervasive in academia and
industry, matching and surpassing human performance on a wide variety of fields
and related tasks. However, just as humans, even the largest artificial neural
networks make mistakes, and once-correct predictions can become invalid as the
world progresses in time. Augmenting datasets with samples that account for
mistakes or up-to-date information has become a common workaround in practical
applications. However, the well-known phenomenon of catastrophic forgetting
poses a challenge in achieving precise changes in the implicitly memorized
knowledge of neural network parameters, often requiring a full model
re-training to achieve desired behaviors. That is expensive, unreliable, and
incompatible with the current trend of large self-supervised pre-training,
making it necessary to find more efficient and effective methods for adapting
neural network models to changing data. To address this need, knowledge editing
is emerging as a novel area of research that aims to enable reliable,
data-efficient, and fast changes to a pre-trained target model, without
affecting model behaviors on previously learned tasks. In this survey, we
provide a brief review of this recent artificial intelligence field of
research. We first introduce the problem of editing neural networks, formalize
it in a common framework and differentiate it from more notorious branches of
research such as continuous learning. Next, we provide a review of the most
relevant knowledge editing approaches and datasets proposed so far, grouping
works under four different families: regularization techniques, meta-learning,
direct model editing, and architectural strategies. Finally, we outline some
intersections with other fields of research and potential directions for future
works.
