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颇具吸引力的能力，它允许我们修正预训练期间错误学习的知识，并利用不断增长的新事实列表来更新模型。虽然现有的模型编辑技术已展现出潜力，但它们通常仅通过一次或少数几次编辑的可靠性、特异性和泛化性指标进行评估。我们认为，为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，我们大规模评估了当前的模型编辑方法，重点关注两种最先进的方法：ROME和MEMIT。我们发现，当模型按顺序编辑多个事实时，它会持续遗忘先前编辑的事实以及执行下游任务的能力。这种遗忘分两个阶段发生——先是渐进但持续性的遗忘阶段，随后是突然或灾难性的遗忘阶段。渐近性遗忘和灾难性遗忘都限制了模型编辑方法在大规模应用中的实用性——前者使得在模型上进行多次编辑时编辑效果降低，后者则限制了此类模型编辑方法的可扩展性。我们的分析还强调了ROME和MEMIT在大规模应用中的其他关键局限性。通过本研究，我们倡导在开发与评估模型编辑方法时，必须将可扩展性纳入考量。