Efficiently Updating Domain Knowledge in Large Language Models: Techniques for Knowledge Injection without Comprehensive Retraining

Abstract Recent advancements in natural language processing have highlighted the critical importance of efficiently updating pre-trained models with domain-specific knowledge. Traditional methods requiring comprehensive retraining are resource-intensive and impractical for many applications. The proposed techniques for knowledge injection, including the integration of adapter layers, retrieval-augmented generation (RAG), and knowledge distillation, offer a novel and significant solution to this challenge by enabling efficient updates without extensive retraining. Adapter layers allow for specialized fine-tuning, preserving the model's original capabilities while incorporating new information. RAG enhances the contextual relevance of generated responses by dynamically retrieving pertinent information from a domain-specific knowledge base. Knowledge distillation transfers specialized knowledge from smaller models to the larger pre-trained model, augmenting its performance in new domains. Experimental results demonstrated substantial improvements in accuracy, precision, recall, and F1-score, along with enhanced contextual relevance and coherence. The findings demonstrate the potential of the proposed methods to maintain the relevance and accuracy of language models in dynamic, information-rich environments, making them particularly useful in fields requiring timely and accurate information.