CAKE: Causal-Guided Adaptive Knowledge Editing for LLMs
Shuxin Liu, Jianhao Zhang
LLMs have static pre-trained knowledge, leading to obsolescence and hallucinations. Knowledge Editing (KE) addresses these issues and typically requires multi-layer modifications. However, existing state-of-the-art methods, largely following the Locate–Select–Assign–Edit (LSAE) paradigm, rely on fixed-layer selection and uniform residual assignment, ignoring the heterogeneous causal efficacy of different layers. To bridge this, we propose \textbf{CAKE} (\textbf{C}ausal-Guided \textbf{A}daptive \textbf{K}nowledge \textbf{E}diting), a collaborative editing method within the more general Locate–Weight–Assign–Edit (LWAE) paradigm that: (1) selectively identifies critical layers via causal tracing scores; and (2) adaptively allocates editing burdens based on causal weights rather than uniform assumptions. We formulate residual assignment as a constrained quadratic optimization problem and derive a solution for optimal residual allocation, showing that aligning edits with causal efficacy mitigates recursive error accumulation. Furthermore, we establish a generalized weight shift error bound, under which existing paradigms emerge as special, restricted cases. Experimental results demonstrate that CAKE achieves SOTA performance with comparable overhead, validating the superiority of causal-guided adaptation.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表