The Role of Generative AI in Natural Language Processing Applications

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.