  In recent times, the grandeur of Large Language Models (LLMs) has not only
shone in the realm of natural language processing but has also cast its
brilliance across a vast array of applications. This remarkable display of LLM
capabilities has ignited a surge in research contributions within this domain,
spanning a diverse spectrum of topics. These contributions encompass
advancements in neural network architecture, context length enhancements, model
alignment, training datasets, benchmarking, efficiency improvements, and more.
Recent years have witnessed a dynamic synergy between academia and industry,
propelling the field of LLM research to new heights. A notable milestone in
this journey is the introduction of ChatGPT, a powerful AI chatbot grounded in
LLMs, which has garnered widespread societal attention. The evolving technology
of LLMs has begun to reshape the landscape of the entire AI community,
promising a revolutionary shift in the way we create and employ AI algorithms.
Given this swift-paced technical evolution, our survey embarks on a journey to
encapsulate the recent strides made in the world of LLMs. Through an
exploration of the background, key discoveries, and prevailing methodologies,
we offer an up-to-the-minute review of the literature. By examining multiple
LLM models, our paper not only presents a comprehensive overview but also
charts a course that identifies existing challenges and points toward potential
future research trajectories. This survey furnishes a well-rounded perspective
on the current state of generative AI, shedding light on opportunities for
further exploration, enhancement, and innovation.
