UniAdapt: A Universal Adapter for Knowledge Calibration
Keywords: Lifelong model editing, Mixture of experts, Retrieval-Augmented Generation
Abstract: Large Language Models (LLMs) require frequent updates to correct errors and keep pace with continuously evolving knowledge in a timely and effective manner. Recent research in *it model editing* has highlighted the challenges in balancing generalization and locality, especially in the context of *lifelong model editing*. We discover that inserting knowledge directly into the model often causes conflicts and potentially disrupts other unrelated pre-trained knowledge. To address this problem, we introduce UniAdapt, a universal adapter for knowledge calibration. Inspired by the Mixture of Experts architecture and Retrieval-Augmented Generation, UniAdapt is designed with a vector-assisted router that is responsible for routing inputs to appropriate experts. The router maintains a vector store, including multiple shards, to construct routing vectors based on semantic similarity search results. UniAdapt is fully model-agnostic and designed for seamless plug-and-play integration. Experimental results show that UniAdapt outperforms existing lifelong model editors and achieves exceptional results in most metrics.
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Primary Area: other topics in machine learning (i.e., none of the above)
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Submission Number: 5768
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