Merging Mixture of Experts and Retrieval Augmented Generation for Enhanced Information Retrieval and Reasoning

Abstract This study investigates the integration of Retrieval Augmented Generation (RAG) into the Mistral 8x7B Large Language Model (LLM), which already uses Mixture of Experts (MoE), to address its existing limitations in complex information retrieval and reasoning tasks. By leveraging the Google BIG-Bench dataset, we conducted extensive quantitative and qualitative analyses to evaluate the augmented model's performance. The results demonstrate significant improvements in accuracy, precision, recall, and F1 score, highlighting the enhanced model's superior capability to generate contextually rich, accurate, and nuanced responses. This integration showcases a promising approach to overcoming the intrinsic limitations of traditional LLMs, marking a pivotal advancement in artificial intelligence research. Our findings contribute to the ongoing development of more adaptable, efficient, and intelligent AI systems, opening new avenues for AI application across various fields. The study acknowledges limitations related to dataset scope and computational demands, suggesting directions for future research to further refine and expand the model's applicability.