Large language models (LLMs) can effectively handle outdated information through knowledge editing.However, current approaches face two key limitations: (I) Poor generalization: Most approaches rigidly inject new knowledge without ensuring that the model can use it effectively to solve practical problems.(II) Narrow scope: Current methods focus primarily on structured fact triples, overlooking the diverse unstructured forms of factual information (e.g., news, articles) prevalent in real-world contexts.To address these challenges, we propose a new paradigm: teaching LLMs to edit knowledge via Chain of Thoughts (CoTs) reasoning (CoT2Edit).We first leverage language model agents for both structured and unstructured edited data to generate CoTs, building high-quality instruction data.The model is then trained to reason over edited knowledge through supervised finetuning (SFT) and Group Relative Policy Optimization (GRPO).At inference time, we integrate Retrieval-Augmented Generation (RAG) to dynamically retrieve relevant edited facts for real-time knowledge editing.Experimental results demonstrate that our method achieves strong generalization across six diverse knowledge editing scenarios with just a single round of training on three open-source language models.The codes are available at https:// github.com/FredJDean/CoT2Edit.

In recent years, the study of real-world graphs has revealed their inherent heterogeneity, prompting growing research interest in heterogeneous graphs. Characterized by diverse node and relation types, heterogeneous graphs have led to the development of heterogeneous graph neural networks , which possess the remarkable ability of modeling such heterogeneity. Consequently, researchers have embraced these networks, applying them in various domains. A prevalent approach is using meta-path based methods in heterogeneous graph neural networks . However, a significant limitation arises from the fact that such methods tend to overlook vital attribute information within intermediate nodes and disregard relevant semantics across various meta-paths. To address the above limitations, we propose a new model named HGNN-MRCS. Specifically, HGNN-MRCS incorporates three key components, i.e., a relation aware module to encapsulate the attribute information of the intermediate nodes; a meta-path aware technique to facilitate learning of semantic information of each meta-path and enable higher-order representation learning ; and a knowledge distillation strategy to learn relevant semantics across meta-paths and fuse them. Experimental results on four real-world datasets demonstrate the superior performance of this work over the SOAT methods. The source codes of this work are available at https://github.com/ZZY-GraphMiningLab/HGNN-MRCS .