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.

翻译：在大型语言模型中编辑知识是一项具有吸引力的能力，它允许我们纠正预训练过程中错误学习的事实，并持续用新事实更新模型。尽管现有模型编辑技术已展现出潜力，但通常仅通过单次或少量编辑的可靠性、特异性和泛化性指标进行评估。我们认为，要使模型编辑具有实际效用，必须能够对同一模型进行多次编辑。基于此，我们系统评估了当前模型编辑方法在大规模场景下的表现，重点聚焦两种最先进方法：ROME和MEMIT。研究发现，当模型被依次编辑多个事实时，会持续遗忘先前编辑过的事实以及执行下游任务的能力。这种遗忘呈现两个阶段——初始渐进性遗忘阶段，随后是突发性或灾难性遗忘阶段。渐进性和灾难性遗忘都限制了模型编辑方法在大规模场景下的实用性：前者使多次编辑后模型编辑效果逐步降低，后者则从根本上制约了此类方法的可扩展性。我们的分析还揭示了ROME和MEMIT在大规模场景下的其他关键局限性。通过本工作，我们倡导在保持可扩展性的前提下开发与评估模型编辑方法。