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.

翻译：深度神经网络在学术界和工业界日益普及，在众多领域及相关任务中达到甚至超越人类表现。然而，与人类相似，即使是最大的人工神经网络也会犯错，且随着世界发展，曾经正确的预测可能失效。在实际应用中，用包含纠错或最新信息的样本扩充数据集已成为常见应对策略。但灾难性遗忘这一熟知现象使得精准修改神经网络参数中隐式记忆的知识面临挑战，通常需要完全重新训练模型才能实现预期行为。这种方式成本高昂、可靠性不足，且与当前大规模自监督预训练的趋势相悖，因此亟需更高效、更有效的方法来使神经网络模型适应动态变化的数据。为满足这一需求，知识编辑作为新兴研究领域应运而生，旨在无需影响模型在已学习任务上的表现的前提下，实现对预训练模型可靠、数据高效且快速的修改。本综述对该人工智能新兴领域进行简要回顾。首先介绍神经网络编辑问题，在统一框架下对其进行形式化定义，并将其与持续学习等更广为人知的研究方向相区分。其次，综述目前最为相关的知识编辑方法与数据集，将相关工作归为四大类：正则化技术、元学习、直接模型编辑和架构策略。最后，概述该领域与其他研究方向的交叉点及潜在未来方向。