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 modula
The proliferation of increasingly large language models necessitates more efficient and flexible methods for knowledge injection to overcome limitations of existing approaches like catastrophic forgetting and costly updates.
Improving knowledge injection capabilities directly impacts the utility, specificity, and updateability of large language models, making them more adaptable to domain-specific and time-sensitive information without constant retraining.
This research introduces a modular approach to knowledge injection in LLMs, allowing for more flexible integration of external knowledge while mitigating common issues faced by current methods.
- · AI developers
- · Enterprises deploying LLMs
- · Specialized AI applications
- · Traditional monolithic LLM architectures
- · Inefficient fine-tuning methods
Large language models become more agile and customizable for specific tasks and continuously updated knowledge bases.
Reduced computational costs and time for adapting LLMs to new information, accelerating development cycles.
Potentially enables LLMs to maintain relevance and accuracy in fast-evolving fields with less resource expenditure, promoting broader adoption in dynamic sectors.
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