Generative AI for Natural Language Processing and Understanding

Abstract

Generative AI has significantly transformed the field of Natural Language Processing (NLP) by enhancing capabilities in text generation, comprehension, and translation. This paper provides a comprehensive overview of the key generative models, including Transformer-based architectures such as GPT (Generative Pre-trained Transformer), BERT (Bidirectional Encoder Representations from Transformers), and T5 (Text-To-Text Transfer Transformer). We delve into the mechanisms behind these models, their training methodologies, and their impact on various NLP tasks. The paper also explores applications such as text generation, machine translation, text summarization, and question answering systems, highlighting the advancements and improvements achieved through generative AI. Additionally, we address the challenges associated with these models, including issues of coherence, computational resource demands, and ethical concerns such as bias and misuse. The study concludes with a discussion on future directions for research, emphasizing the need for innovations in model architecture, training techniques, and strategies to address ethical considerations. This paper aims to provide a thorough understanding of generative AI's role in NLP and its potential for driving future advancements in the field.