Editing knowledge in large language models is an attractive capability to
have which allows us to correct incorrectly learnt facts during pre-training,
as well as update the model with an ever-growing list of new facts. While
existing model editing techniques have shown promise, they are usually
evaluated using metrics for reliability, specificity and generalization over
one or few edits. We argue that for model editing to have practical utility, we
must be able to make multiple edits to the same model. With this in mind, we
evaluate the current model editing methods at scale, focusing on two state of
the art methods: ROME and MEMIT. We find that as the model is edited
sequentially with multiple facts, it continually forgets previously edited
facts and the ability to perform downstream tasks. This forgetting happens in
two phases -- an initial gradual but progressive forgetting phase followed by
abrupt or catastrophic forgetting phase. Both gradual and catastrophic
forgetting limit the usefulness of model editing methods at scale -- the former
making model editing less effective as multiple edits are made to the model
while the latter caps the scalability of such model editing methods. Our
analysis also highlights other key limitations of ROME and MEMIT at scale. With
our work, we push for the development and evaluation of model editing methods
keeping scalability in mind.

大型语言模型的编辑知识是一种有吸引力能力，允许我们在预训练期间纠正学习不正确的事实，并在模型中更新日益增长的新事实清单。然而，现有的模型编辑技术通常使用可靠性、特异性和对一项或少数几个编辑的泛化性指标进行评估。我们认为，为了使模型编辑具有实际效用，我们必须能够对同一模型进行多次编辑。针对当前的两种最先进的方法 ——ROME 和 MEMIT，我们在大规模上评估模型编辑方法。我们发现，随着模型被连续地编辑多个事实，它会不断遗忘先前编辑的事实以及执行下游任务的能力。这种遗忘分为两个阶段 —— 一个初始的逐渐但渐进性的遗忘阶段，后面是突然或灾难性的遗忘阶段。逐渐和灾难性的遗忘都限制了大规模上模型编辑方法的有效性和可扩展性。我们的分析还强调了 ROME 和 MEMIT 在大规模上的其他关键局限性。通过我们的工作，我们推动以可扩展性为中心的模型编辑方法的开发和评估。