Enhancing Contextual Understanding of Mistral LLM with External Knowledge Bases

Abstract

This study explores the enhancement of contextual understanding and factual accuracy in Language Learning Models (LLMs), specifically Mistral LLM, through the integration of external knowledge bases. We developed a novel methodology for dynamically incorporating real-time information from diverse external sources, aiming to address the inherent limitations of LLMs rooted in their training datasets. Our experiments demonstrated significant improvements in accuracy, precision, recall, and F1 score, alongside qualitative enhancements in response relevance and factual accuracy. The research also tackled the computational challenges of integrating external knowledge, ensuring the model's efficiency and practical applicability. This work not only highlights the potential of external knowledge bases to augment the capabilities of LLMs but also sets the stage for future advancements in creating more intelligent, adaptable, and contextually aware AI systems. The findings contribute to the broader field of AI and NLP by offering insights into overcoming traditional limitations of LLMs, presenting a significant step toward developing AI systems with enhanced real-world applicability and knowledge accessibility.