Rethinking the Residual Distribution of Locate-then-Editing Methods in Model Editing

• URL: http://arxiv.org/abs/2502.03748v2
• Date: Mon, 13 Oct 2025 01:45:48 GMT
• Title: Rethinking the Residual Distribution of Locate-then-Editing Methods in Model Editing
• Authors: Xiaopeng Li, Shanwen Wang, Shasha Li, Shezheng Song, Bin Ji, Jun Ma, Jie Yu,
• Abstract summary: Model editing enables targeted updates to the knowledge of large language models.<n> locate-then-edit methods first identify critical layers, then compute residuals at the final critical layer based on the target edit.<n> residual distribution, a core mechanism in these methods, introduces weight shift errors that undermine editing precision.<n>We propose the BLUE strategy to enhance locate-then-edit methods.
• Score: 14.958557185068
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Model editing enables targeted updates to the knowledge of large language models (LLMs) with minimal retraining. Among existing approaches, locate-then-edit methods constitute a prominent paradigm: they first identify critical layers, then compute residuals at the final critical layer based on the target edit, and finally apply least-squares-based multi-layer updates via $\textbf{residual distribution}$. While empirically effective, we identify a counterintuitive failure mode: residual distribution, a core mechanism in these methods, introduces weight shift errors that undermine editing precision. Through theoretical and empirical analysis, we show that such errors increase with the distribution distance, batch size, and edit sequence length, ultimately leading to inaccurate or suboptimal edits. To address this, we propose the $\textbf{B}$oundary $\textbf{L}$ayer $\textbf{U}$pdat$\textbf{E (BLUE)}$ strategy to enhance locate-then-edit methods. Sequential batch editing experiments on three LLMs and two datasets demonstrate that BLUE not only delivers an average performance improvement of 35.59\%, significantly advancing the state of the art in model editing, but also enhances the preservation of LLMs' general capabilities. Our code is available at https://github.com/xpq-tech/BLUE.

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