ChatGPT Alternative Solutions: Large Language Models Survey

• URL: http://arxiv.org/abs/2403.14469v1
• Date: Thu, 21 Mar 2024 15:16:50 GMT
• Title: ChatGPT Alternative Solutions: Large Language Models Survey
• Authors: Hanieh Alipour, Nick Pendar, Kohinoor Roy,
• Abstract summary: Large Language Models (LLMs) have ignited a surge in research contributions within this domain. Recent years have witnessed a dynamic synergy between academia and industry, propelling the field of LLM research to new heights. This survey furnishes a well-rounded perspective on the current state of generative AI, shedding light on opportunities for further exploration, enhancement, and innovation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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