模型編輯中的正確微調

Fine-tuning Done Right in Model Editing

September 26, 2025

作者: Wanli Yang, Fei Sun, Rui Tang, Hongyu Zang, Du Su, Qi Cao, Jingang Wang, Huawei Shen, Xueqi Cheng

cs.AI

摘要

微調作為適應大型語言模型的基礎方法，長期以來被認為在模型編輯方面效果不佳。本文挑戰這一觀點，認為所報告的失敗並非源於微調本身的固有局限，而是源於將其應用於編輯任務的順序特性時，採用了單次深度優先的流程，即在處理下一個樣本前將每個樣本優化至收斂。儘管這種深度優先流程直觀易懂，但結合逐樣本更新會過度優化每次編輯，並引發編輯間的干擾。我們的對照實驗表明，僅需將微調恢復至標準的廣度優先（即基於epoch的）流程，並採用小批量優化，即可顯著提升其在模型編輯中的效果。此外，編輯中的微調還受到先前方法遺留的次優調參位置的影響。通過系統分析調參位置，我們提出了LocFT-BF，這是一種基於恢復微調框架的簡單而有效的局部化編輯方法。跨多種大型語言模型和數據集的廣泛實驗表明，LocFT-BF大幅領先於現有最先進的方法。值得注意的是，據我們所知，它是首個能夠在不犧牲通用能力的情況下，支持10萬次編輯和720億參數模型的方法，這一規模是先前實踐的10倍。通過澄清長期以來的誤解並引入有原則的局部化調參策略，我們將微調從被低估的基線方法提升為模型編輯的領先方法，為未來研究奠定了堅實基礎。

English

Fine-tuning, a foundational method for adapting large language models, has long been considered ineffective for model editing. Here, we challenge this belief, arguing that the reported failure arises not from the inherent limitation of fine-tuning itself, but from adapting it to the sequential nature of the editing task, a single-pass depth-first pipeline that optimizes each sample to convergence before moving on. While intuitive, this depth-first pipeline coupled with sample-wise updating over-optimizes each edit and induces interference across edits. Our controlled experiments reveal that simply restoring fine-tuning to the standard breadth-first (i.e., epoch-based) pipeline with mini-batch optimization substantially improves its effectiveness for model editing. Moreover, fine-tuning in editing also suffers from suboptimal tuning parameter locations inherited from prior methods. Through systematic analysis of tuning locations, we derive LocFT-BF, a simple and effective localized editing method built on the restored fine-tuning framework. Extensive experiments across diverse LLMs and datasets demonstrate that LocFT-BF outperforms state-of-the-art methods by large margins. Notably, to our knowledge, it is the first to sustain 100K edits and 72B-parameter models,10 x beyond prior practice, without sacrificing general capabilities. By clarifying a long-standing misconception and introducing a principled localized tuning strategy, we advance fine-tuning from an underestimated baseline to a leading method for model editing, establishing a solid foundation for future research.