EXPLORING THE ROLE OF GENERATIVE MODELS IN NATURAL LANGUAGE UNDERSTANDING

Abstract

The aim of this paper is to examine how natural language understanding is evolving, paying particular attention to the important contributions made by generative models. Many applications, such as machine translation, generative models in natural language understanding, GPT-3, and language generation, applications of generative models in natural language understanding, and machine translation, depend on natural language understanding. The field has undergone a revolution thanks to generative models, especially deep learning architectures like transformers and recurrent neural networks (RNNs), which enable the creation of text that resembles that of a human and improve language understanding abilities.

Through a thorough analysis of the literature and experimental studies, this paper clarifies the underlying ideas and mechanisms of generative models in the context of understanding natural language. It talks about the developments in transfer learning, fine-tuning methods, and generative pre-training.

The paper presents a critical analysis of the current state of the art and suggests potential directions for future research in the quest to harness generative models for improved natural language understanding while ensuring fairness and transparency. It also highlights the challenges and limitations associated with generative models, including ethical concerns, biases, and interpretability issues.

Keywords: Generative Models, Natural Language Understanding, Artificial Intelligence, Machine Translation, Language Generation.