模型微调会严重遗忘编辑过的知识，需改进方法增强记忆稳定性。
Retention analysis of edited knowledge after fine-tuning
- 分析不同微调目标对模型编辑知识的影响机制。
- 编辑知识在微调中遗忘率远高于预训练知识。
- 用改写句或冻结相关层可显著提升知识保留效果。
大型语言模型（LLMs）存储大量知识，常需更新以修正事实错误、融入新信息或调整行为。模型编辑方法成为高效解决方案，可在极低计算成本下实现局部精准修改。同时，LLMs常用于下游任务的微调。然而，微调对已有编辑知识的影响尚不明确。本文系统研究了不同微调目标与多种编辑技术的交互作用。结果表明，编辑知识在微调过程中比预训练获得的固有知识更易遗忘。该分析揭示了当前编辑方法的关键局限，并强调在下游微调下评估编辑鲁棒性对实际部署至关重要。进一步发现，通过增加编辑内容的改写版本，或在微调阶段冻结相关层，可显著提升知识保留能力，为开发更鲁棒的编辑算法提供思路。
原文摘要 · Abstract (English)
Large language models (LLMs) store vast amounts of knowledge, which often requires updates to correct factual errors, incorporate newly acquired information, or adapt model behavior. Model editing methods have emerged as efficient solutions for such updates, offering localized and precise knowledge modification at significantly lower computational cost than continual training. In parallel, LLMs are frequently fine-tuned for a wide range of downstream tasks. However, the effect of fine-tuning on previously edited knowledge remains poorly understood. In this work, we systematically investigate how different fine-tuning objectives interact with various model editing techniques. Our findings show that edited knowledge is substantially more susceptible to forgetting during fine-tuning than intrinsic knowledge acquired through pre-training. This analysis highlights a key limitation of current editing approaches and suggests that evaluating edit robustness under downstream fine-tuning is critical for their practical deployment. We further find that knowledge retention can be significantly improved by either augmenting edit knowledge with paraphrases or by freezing layers associated with edited content in fine-tuning stage, offering insight for developing more robust editing algorithms.
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