Learning to Edit (LTE) is a framework designed to enable large language models (LLMs) to effectively incorporate updated knowledge into their responses without negatively impacting their performance on unrelated tasks. The LTE framework consists of two phases: the Alignment Phase and the Inference Phase. In the Alignment Phase, LLMs are fine-tuned on a parallel dataset that includes both in-scope and out-of-scope queries, allowing them to learn how to apply updated knowledge while preserving the integrity of unrelated information. In the Inference Phase, a retrieval-based mechanism is used to dynamically apply updated knowledge to queries, enabling real-time and mass knowledge editing. The LTE framework outperforms existing knowledge editing methods in terms of edit success, portability, fluency, and robustness in both batch and sequential editing scenarios. It also demonstrates minimal interference on general tasks and achieves fast editing speeds. The framework is evaluated across four knowledge editing benchmarks and two LLM architectures, showing significant improvements over existing methods. The LTE method is also effective in general tasks, maintaining performance in unrelated domains such as commonsense reasoning and world knowledge. The LTE framework is compared with seven advanced baselines, including SERAC, ICE, MEND, ROME, MEMIT, FT-L, and FT. The results show that LTE achieves superior performance in knowledge editing tasks, with notable improvements over the current state-of-the-art method SERAC. The framework is also effective in out-of-distribution scenarios, demonstrating robustness and adaptability. The LTE method is further validated through case studies, showing its ability to apply updated knowledge to answer complex queries while maintaining fluency. The LTE framework is also evaluated for its efficiency and effectiveness in terms of time and computational resources. It is shown to be faster than other methods, with a significantly reduced inference time. The framework is also effective in cross-lingual and multimodal editing scenarios, highlighting its versatility. The LTE framework is a novel approach for effective and efficient knowledge editing of LLMs, demonstrating superior performance in various benchmarks and scenarios.Learning to Edit (LTE) is a framework designed to enable large language models (LLMs) to effectively incorporate updated knowledge into their responses without negatively impacting their performance on unrelated tasks. The LTE framework consists of two phases: the Alignment Phase and the Inference Phase. In the Alignment Phase, LLMs are fine-tuned on a parallel dataset that includes both in-scope and out-of-scope queries, allowing them to learn how to apply updated knowledge while preserving the integrity of unrelated information. In the Inference Phase, a retrieval-based mechanism is used to dynamically apply updated knowledge to queries, enabling real-time and mass knowledge editing. The LTE framework outperforms existing knowledge editing methods in terms of edit success, portability, fluency, and robustness in both batch and sequential editing scenarios. It also demonstrates minimal interference on general tasks and achieves fast editing speeds. The framework is evaluated across four knowledge editing benchmarks and two LLM architectures, showing significant improvements over existing methods. The LTE method is also effective in general tasks, maintaining performance in unrelated domains such as commonsense reasoning and world knowledge. The LTE framework is compared with seven advanced baselines, including SERAC, ICE, MEND, ROME, MEMIT, FT-L, and FT. The results show that LTE achieves superior performance in knowledge editing tasks, with notable improvements over the current state-of-the-art method SERAC. The framework is also effective in out-of-distribution scenarios, demonstrating robustness and adaptability. The LTE method is further validated through case studies, showing its ability to apply updated knowledge to answer complex queries while maintaining fluency. The LTE framework is also evaluated for its efficiency and effectiveness in terms of time and computational resources. It is shown to be faster than other methods, with a significantly reduced inference time. The framework is also effective in cross-lingual and multimodal editing scenarios, highlighting its versatility. The LTE framework is a novel approach for effective and efficient knowledge editing of LLMs, demonstrating superior performance in various benchmarks and scenarios.