ChatGPT Alternative Solutions: Large Language Models Survey
Hanieh Alipour Nick Pendar Kohinoor Roy
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.