  Knowledge editing techniques, aiming to efficiently modify a minor proportion
of knowledge in large language models (LLMs) without negatively impacting
performance across other inputs, have garnered widespread attention. However,
existing methods predominantly rely on memorizing the updated knowledge,
impeding LLMs from effectively combining the new knowledge with their inherent
knowledge when answering questions. To this end, we propose a Learning to Edit
(LTE) framework, focusing on teaching LLMs to apply updated knowledge into
input questions, inspired by the philosophy of "Teach a man to fish." LTE
features a two-phase process: (i) the Alignment Phase, which fine-tunes LLMs on
a meticulously curated parallel dataset to make reliable, in-scope edits while
preserving out-of-scope information and linguistic proficiency; and (ii) the
Inference Phase, which employs a retrieval-based mechanism for real-time and
mass knowledge editing. By comparing our approach with seven advanced baselines
across four popular knowledge editing benchmarks and two LLM architectures, we
demonstrate LTE's superiority in knowledge editing performance, robustness in
both batch and sequential editing, minimal interference on general tasks, and
rapid editing speeds. The data and code are available at
https://github.com/YJiangcm/LTE.
