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：对大型语言模型进行知识编辑是一项具有吸引力的能力，它使我们能够修正预训练期间错误学习的事实，并持续更新模型以纳入新增事实。现有模型编辑技术虽已展现潜力，但通常仅通过可靠性、特异性和泛化性指标评估单次或少量编辑。我们认为，要使模型编辑具备实际效用，必须支持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，当模型被连续编辑多个事实时，其不仅持续遗忘先前编辑过的事实，还会逐步丧失执行下游任务的能力。这种遗忘呈现双阶段特征——初始为渐进式递进遗忘，随后进入突发性或灾难性遗忘阶段。两种遗忘模式均限制了模型编辑方法在大规模场景下的实用性：前者导致多次编辑后效率显著降低，后者则从根本上限制了此类方法的可扩展性。我们的分析还揭示了ROME与MEMIT在大规模应用中的其他关键缺陷。通过本研究，我们倡导模型编辑方法的开发与评估应优先考虑可扩展性。