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

2026-04-07

AI Summary

This work addresses the limited generalization capability of existing knowledge editing methods for large language models and their difficulty in handling unstructured knowledge. The authors propose CoT2Edit, a novel paradigm that introduces instruction-guided chain-of-thought (CoT) reasoning into knowledge editing for the first time. CoT2Edit leverages language model agents to automatically generate CoT instruction data and integrates supervised fine-tuning (SFT), Group Relative Policy Optimization (GRPO), and retrieval-augmented generation (RAG) to enable unified editing and application of both structured and unstructured knowledge. Evaluated on three open-source large language models, CoT2Edit achieves significant improvements in generalization across six diverse knowledge editing scenarios after only a single round of training.

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Large language models (LLMs) can effectively handle outdated information through knowledge editing.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.

Problem

• knowledge editing
• generalization
• unstructured data
• large language models
• fact triples

Innovation

• Chain-of-Thought Prompting
• Knowledge Editing
• Retrieval-Augmented Generation
• Supervised Fine-Tuning
• Group Relative Policy Optimization