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In the evolving landscape of Natural Language Processing (NLP), the emergence of large language models has redefined the boundaries of human-computer interaction. This paper presents an in-depth review of ChatGPT, a pioneering exemplar in this domain. We commence with a thorough discussion on the evolution of NLP techniques and models, culminating in the inception of ChatGPT. The critical examination of pertinent research papers, projects, and benchmarks showcases the progression of large language models in the context of complex language understanding and generation tasks. The primary focus of this paper is to elucidate the intricate methodology underpinning ChatGPT's architecture and technology. We meticulously outline the comprehensive training process encompassing pretraining and fine-tuning phases, shedding light on the nuanced decisions that bolster model performance. The dataset employed for training and validation is delineated, contributing to an informed understanding of the model's capabilities. The user experience and feedback section encapsulates the empirical perspectives of interacting with ChatGPT, elucidating its strengths and limitations. The paper also prognosticates on the challenges and future directions for ChatGPT. The extant limitations are outlined, and plausible avenues for research and development are suggested to propel the model's potential. In conclusion, this review synthesizes the contributions of ChatGPT in the NLP landscape, underscoring its significance in reshaping the frontiers of language-based human-computer interaction. By amalgamating insights from methodology, applications, ethics, and performance, this paper offers a comprehensive compendium of the evolution and impact of ChatGPT in the realm of NLP research and applications.
Kaswan et al. (Thu,) studied this question.