教大模型通过思维链推理编辑知识，支持多种信息形式。
Learning to Edit Knowledge via Instruction-based Chain-of-Thought Prompting
- 用思维链生成指令数据，训练模型理解新知识
- 单次训练在6个场景中表现良好，支持动态检索更新
- 适合需要实时更新知识的AI应用开发者
大语言模型可有效处理过时信息，但现有方法存在两大局限：（一）泛化能力差：多数方法机械注入新知识，未确保模型能实际运用；（二）适用范围窄：聚焦结构化事实三元组，忽略新闻、文章等非结构化真实信息。为此，我们提出新范式CoT2Edit——通过思维链（CoTs）推理来教授模型编辑知识。首先利用语言模型代理生成结构化与非结构化数据的思维链，构建高质量指令数据；随后通过监督微调（SFT）和组相对策略优化（GRPO）训练模型进行推理。推理时引入检索增强生成（RAG），动态获取相关更新事实实现实时知识编辑。实验表明，该方法仅需一次训练，在三个开源模型上即可在六个不同场景中实现强泛化性能。代码已开源。
原文摘要 · Abstract (English)
Large language models (LLMs) can effectively handle outdated information through knowledge editing.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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