Cross-Domain Knowledge Transfer without Retraining to Facilitating Seamless Knowledge Application in Large Language Models

Abstract Cross-domain knowledge transfer in large language models (LLMs) presents significant challenges, particularly regarding the extensive resources required for retraining. This research introduces innovative embedding adaptation and context adjustment techniques that enable LLMs to efficiently transfer knowledge across diverse domains without the need for comprehensive retraining. Experimental results demonstrate improved model flexibility and reduced computational demands, highlighting the potential for rapid deployment and scalability. These findings suggest a sustainable approach to deploying adaptive AI across various sectors, significantly impacting future developments in artificial intelligence.