The imperative task of revising or updating the knowledge stored within large language models arises from two distinct sources: intrinsic errors inherent in the model which should be corrected and outdated knowledge due to external shifts in the real world which should be updated. Prevailing efforts in model editing conflate these two distinct categories of edits arising from distinct reasons and directly modify the original knowledge in models into new knowledge. However, we argue that preserving the model's original knowledge remains pertinent. Specifically, if a model's knowledge becomes outdated due to evolving worldly dynamics, it should retain recollection of the historical knowledge while integrating the newfound knowledge. In this work, we introduce the task of Temporal Knowledge Editing (TKE) and establish a benchmark AToKe (Assessment of TempOral Knowledge Editing) to evaluate current model editing methods. We find that while existing model editing methods are effective at making models remember new knowledge, the edited model catastrophically forgets historical knowledge. To address this gap, we propose a simple and general framework termed Multi-Editing with Time Objective (METO) for enhancing existing editing models, which edits both historical and new knowledge concurrently and optimizes the model's prediction for the time of each fact. Our assessments demonstrate that while AToKe is still difficult, METO maintains the effectiveness of learning new knowledge and meanwhile substantially improves the performance of edited models on utilizing historical knowledge.

翻译：修订或更新大语言模型中所存储知识的紧迫任务源于两个不同的来源：模型固有的错误性知识需要被修正，以及因现实世界外部变化而过时的知识需要被更新。当前模型编辑领域的努力将这两类源于不同原因的编辑混为一谈，并直接将模型中的原始知识修改为新知识。然而，我们认为保留模型的原始知识仍然具有相关性。具体而言，如果模型的知识因世界动态演变而过时，它应在整合新知识的同时保留对历史知识的记忆。在本工作中，我们引入了时间知识编辑（Temporal Knowledge Editing，TKE）任务，并构建了基准数据集AToKe（Assessment of TempOral Knowledge Editing）以评估当前的模型编辑方法。我们发现，尽管现有模型编辑方法能有效让模型记住新知识，但编辑后的模型会灾难性地遗忘历史知识。为解决这一不足，我们提出了一种简单且通用的框架，称为基于时间目标的多重编辑（Multi-Editing with Time Objective，METO），用于增强现有编辑模型。该框架同时编辑历史知识和新知识，并优化模型对每个事实时间的预测。我们的评估表明，尽管AToKe仍具挑战性，但METO在保持学习新知识有效性的同时，大幅提升了编辑模型在利用历史知识方面的性能。