Introducing Super RAGs in Mistral 8x7B-v1
Ayush Thakur Raghav Gupta
The relentless pursuit of enhancing Large Language Models (LLMs) has led to
the advent of Super Retrieval-Augmented Generation (Super RAGs), a novel
approach designed to elevate the performance of LLMs by integrating external
knowledge sources with minimal structural modifications. This paper presents
the integration of Super RAGs into the Mistral 8x7B v1, a state-of-the-art LLM,
and examines the resultant improvements in accuracy, speed, and user
satisfaction. Our methodology uses a fine-tuned instruct model setup and a
cache tuning fork system, ensuring efficient and relevant data retrieval. The
evaluation, conducted over several epochs, demonstrates significant
enhancements across all metrics. The findings suggest that Super RAGs can
effectively augment LLMs, paving the way for more sophisticated and reliable AI
systems. This research contributes to the field by providing empirical evidence
of the benefits of Super RAGs and offering insights into their potential
applications.