Think and Recall: Layer-Level Prompting for Lifelong Model Editing

Lifelong model editing aims to dynamically adjust a model's output concerning specific facts, knowledge items, or behaviors, enabling the model to adapt to the evolving demands of realworld applications. While some retrieval-based methods have demonstrated potential in lifelong editing scenarios by storing edited knowledge in external memory, they often suffer from limitations in usability, such as requiring additional training corpora or lacking support for reversible and detachable edits. To address these issues, we propose a plug-and-play method for knowledge retrieval and injection, i.e., Layer-Level Prompting (LLP), which enables seamless and efficient lifelong model editing. In our LLP framework, the reasoning process of LLMs is divided into two stages, respectively, knowledge retrieval (Thinking) and knowledge injection (Recalling). Specifically, the knowledge retrieval process is performed in the early layers of the model, using layer outputs as thinking clues. And access the updated knowledge from memory in the subsequent layer to complete the knowledge injection process. Experimental results demonstrate that our method consistently outperforms existing techniques on lifelong model editing tasks, achieving superior performance on question answering and hallucination benchmarks across different LLMs. Our code is available at: https://github.com/wjkwjkwjkwjk/LLP .