Large language models (LLMs) struggle in specialized fields like healthcare, finance, and law due to limited domain knowledge. Traditional solutions like Domain Adaptive Pre-training (DAPT) and Retrieval-Augmented Generation (RAG) face challenges, including high computational costs and inefficiencies. A novel approach, the “Memory Decoder,” developed by researchers at Shanghai Jiao Tong University and Shanghai AI Lab, addresses these limitations. This plug-and-play pre-trained memory module integrates seamlessly with various LLMs without changing original parameters, enabling quicker domain adaptation. It significantly enhances performance in specific fields while maintaining general capabilities and reducing computational overhead. Experimental results indicate an average perplexity reduction of 6.17% across Qwen and Llama models. The Memory Decoder excels in cross-model adaptation and efficiently transfers knowledge between different architectures. Despite some computational overhead in the pre-training phase, its innovative approach may revolutionize domain adaptation and improve LLM efficiency in professional applications.
