ChatGPT Alternative Solutions: Large Language Models Survey
<|reference_start|>ChatGPT Alternative Solutions: Large Language Models Survey: 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.<|reference_end|>
@article{alipour2024chatgpt,
    title={ChatGPT Alternative Solutions: Large Language Models Survey},
    author={Hanieh Alipour, Nick Pendar, Kohinoor Roy},
    journal={David C. Wyld et al. (Eds): NBIoT, MLCL, NMCO, ARIN, CSITA, ISPR,
  NATAP-2024. pp. 153-173, 2024. CS & IT - CSCP 2024},
    year={2024},
    doi={10.5121/csit.2024.140514},
    archivePrefix={arXiv},
    eprint={2403.14469},
    primaryClass={cs.CL cs.AI}
}