In-context knowledge editing (IKE) is a promising technique for updating Large Language Models (LLMs) with new information. However, IKE relies on lengthy, fact-specific demonstrations which are costly to create and consume significant context window space. In this paper, we introduce persuasion tokens (P-Tokens) -- special tokens trained to replicate the effect of IKE demonstrations, enabling efficient knowledge editing without requiring fact-specific demonstrations. We evaluate P-Tokens across two editing datasets and three LLMs, demonstrating performance comparable to, and often exceeding, IKE. We further find that editing performance is robust to distractors with small negative effects to neighboring facts, and that increasing the number of P-Tokens improves performance. Our work addresses key limitations of IKE and provides a more practical and scalable alternative for editing LLMs.

翻译：上下文知识编辑是一种用于更新大语言模型知识的有前景的技术。然而，该方法依赖于冗长且针对特定事实的演示示例，这些示例的创建成本高昂且会消耗大量上下文窗口空间。本文提出了说服令牌——一种经过训练以复制上下文知识编辑演示效果的特殊令牌，能够在无需针对特定事实的演示示例的情况下实现高效的知识编辑。我们在两个编辑数据集和三种大语言模型上评估了说服令牌的性能，结果表明其表现与上下文知识编辑相当，且通常更优。我们进一步发现，编辑性能对干扰信息具有鲁棒性，对相邻事实仅产生轻微的负面影响，并且增加说服令牌的数量能够提升性能。本研究解决了上下文知识编辑的关键局限性，为编辑大语言模型提供了一种更实用且可扩展的替代方案。