Learning to Edit Knowledge via Instruction-based Chain-of-Thought Prompting

• URL: http://arxiv.org/abs/2604.05540v1
• Date: Tue, 07 Apr 2026 07:38:25 GMT
• Title: Learning to Edit Knowledge via Instruction-based Chain-of-Thought Prompting
• Abstract summary: Large language models (LLMs) can effectively handle outdated information through knowledge editing.<n>Current approaches rigidly inject new knowledge without ensuring that the model can use it effectively to solve practical problems.<n>We propose a new paradigm: teaching LLMs to edit knowledge via Chain of Thoughts (CoTs) reasoning.
• Score: 18.981986320960754
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) can effectively handle outdated information through knowledge editing.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 fine-tuning (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.

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