Abstract:
: Generative AI has evolved very quickly, and has brought major changes for natural language processing
techniques. This review paper will specifically focus on exploring the many core aspects of NLP that use generative AI
and discussing the most crucial models and methods alongside novelties in the field. We outline the history of
generative models starting with Recurrent Neural Networks and Long Short-Term Memory then jumping to the present
day highly popularized transformer such as the BERT and GPT. This paper will discuss various areas such as text
generation, machine translation, conversational agents, sentiment classifications, and many others.
Besides the example of these applications, the eight individuals provide insight into the technical and ethical
considerations of generative AI. Thus, here are some key technical challenges: A great amount of data is needed;
calculations are rather resource-intensive; and finally, interpretability of the model is critical. Ethical implications are
also important which include; the fairness, some prejudices and bad intentions that may arise from the generative AI
technologies.
In this paper, I assess the achievements and the future prospects of generative AI in NLP by analyzing case studies and
critical perspective of recent strategies. We have highlighted how generative AI brings much potential for furthering
NLP research but at the same underline the need for consistent growth in technology, and maintaining sound ethics in
all fields associated with AI. Through understandings of these aspects, we aim to deliver an objective view on the
current status as well as prospects and directions of the development that could promote both the advancement and
constructive application of generative AI in NLP.
