When to Write and When to Suppress: Route-Specialized Dual Adapters for Memory-Assisted Knowledge Editing
arXiv:2606.14668v1 Announce Type: new 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 prom
The increasing scale and complexity of large language models necessitate more efficient and precise knowledge editing techniques to maintain their utility and adaptability.
This research contributes to making AI models more controllable and reliable, crucial for their integration into critical applications and the reduction of 'hallucinations'.
Knowledge editing moves beyond simple updates to include sophisticated decision-making on when to apply or suppress edits, improving model consistency and reducing unintended side effects.
- · AI developers
- · Enterprises using LLMs
- · AI ethics and safety researchers
- · Inefficient knowledge editing techniques
- · Models prone to 'catastrophic forgetting'
AI models become more adaptable and less prone to factual errors or outdated information.
This improved reliability accelerates the deployment of AI in sensitive domains requiring high accuracy and customizability.
Enhanced knowledge editing could lead to more nuanced and context-aware AI agents, expanding their range of autonomous capabilities.
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Read at arXiv cs.LG