🤖 AI Summary
This work addresses the challenge of knowledge editing, which requires updating specific factual information while preserving unrelated yet semantically proximate knowledge. The authors propose a dual-adapter routing mechanism that employs a relevance router—supporting either lexical or BGE embeddings—to determine whether an input query matches stored edited knowledge. When a match is detected, an edit adapter is activated to prioritize the updated fact; otherwise, a locality adapter maintains the model’s original behavior. By decoupling the decisions of “when to write” and “when to suppress,” this approach enables more precise control over knowledge modifications. Implemented with parameter-efficient LoRA adapters, the method achieves state-of-the-art performance across three thousand-example benchmarks—CF, zsRE, and mQUAKE—on both Llama-3.1-8B-Instruct and Qwen3-8B, attaining a peak accuracy of 0.9922.
📝 Abstract
Knowledge editing systems must update selected facts while preserving nearby but irrelevant behavior. This paper studies this problem in a memory-assisted setting where an edit memory is retrieved at inference time and a parameter-efficient adapter corrects the model's object preference. We argue that the central design question is not only how to write an edit, but also when to suppress it. We introduce \method{}, a route-specialized dual-adapter editor. A relevance router first decides whether a prompt should receive an edit memory. Routed prompts use an edit adapter trained to prefer the new object over the original object; unrouted non-direct prompts use a separate locality adapter trained to preserve or restore the original-object preference. We evaluate \method{} on three 1,000-case protocols, \cf{}, \zsre{}, and \mquake{}, under the same memory protocol and two 7B/8B base models. On Llama-3.1-8B-Instruct, \method{} obtains the best overall probability-preference accuracy on all three benchmarks: 0.8180 on \cf{}, 0.8946 on \zsre{}, and 0.9922 on \mquake{}. The same trend holds on Qwen3-8B. Router ablations show that the relevant memory boundary differs across datasets: a lexical neural router is safest on \cf{}, while BGE embedding routing is better on \zsre{} and \mquake{}. Component and module ablations show that the gain mainly comes from separating edit injection from off-route suppression rather than from simply increasing LoRA capacity.