aiqbench analysis
This paper introduces Decoupled Mixture-of-Experts (DMoE), an architecture designed to inject external knowledge into LLMs without compromising the base model's core capabilities or causing catastrophic forgetting. By decoupling expert modules and routers, DMoE enables independent updates for specific domains while maintaining efficient inference through targeted attachment to final-layer feed-forward networks. The approach offers a promising solution to the flexibility-integration trade-off currently limiting parametric knowledge injection methods in large language models.
Decoupled Mixture-of-Experts for Parametric Knowledge Injection
arXiv:2606.14243v1 Announce Type: new Abstract: Knowledge injection aims to equip large language models (LLMs) with external, domain-specific, or time-sensitive knowledge. Existing approaches typically face a trade-off between flexibility and integration: retrieval-augmented generation keeps knowledge outside the model but only provides prompt-level augmentation, whereas post-training based methods encode new knowledge into shared parameters but may introduce catastrophic forgetting, knowledge conflict, and costly updates. In this paper, we propose Decoupled Mixture-of-Experts (DMoE), a modular architecture for parametric knowledge injection that decouples both experts and the router from the base model. DMoE converts external knowledge corpora into independently updatable expert modules and uses a lightweight uncertainty-aware router to activate relevant experts only when the base model lacks sufficient knowledge during generation. To support efficient auto-regressive inference, DMoE attaches experts only to the final-layer feed-forward network, preserving KV-cache reuse while enabling parameter-level knowledge augmentation. Experiments on knowledge-intensive benchmarks show that DMoE consistently improves answer quality over retrieval and adapter-based baselines.
Abstract:Knowledge injection aims to equip large language models (LLMs) with external, domain-specific, or time-sensitive knowledge. Existing approaches typically face a trade-off between flexibility and integration: retrieval-augmented generation keeps knowledge outside the model but only provides prompt-level augmentation, whereas post-training based methods encode new knowledge into shared parameters but may introduce catastrophic forgetting, knowledge conflict, and costly updates. In this paper, we propose Decoupled Mixture-of-Experts (DMoE), a modular architecture for parametric knowledge injection that decouples both experts and the router from the base model. DMoE converts external knowledge corpora into independently updatable expert modules and uses a lightweight uncertainty-aware router to activate relevant experts only when the base model lacks sufficient knowledge during generation. To support efficient auto-regressive inference, DMoE attaches experts only to the final-layer feed-forward network, preserving KV-cache reuse while enabling parameter-level knowledge augmentation. Experiments on knowledge-intensive benchmarks show that DMoE consistently improves answer quality over retrieval and adapter-based baselines.
Computation and Language (cs.CL)
arXiv:2606.14243 [cs.CL]
(or arXiv:2606.14243v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.14243 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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