ENHANCING NATURAL LANGUAGE PROCESSING THROUGH TRANSFORMER MODELS AND LARGE SCALE PRETRAINED NETWORKS
Abstract
Natural Language Processing (NLP) has seen significant advancements with the introduction of Transformer models and large-scale pre-trained networks. These architectures have enabled improved language understanding, contextual awareness, and generation capabilities, surpassing traditional recurrent and convolutional neural networks. This paper explores the evolution of Transformer-based models such as BERT, GPT, and T5, their impact on various NLP applications, and the challenges associated with scalability, training data, and bias. A comparative analysis of different transformer architectures is presented, highlighting their strengths and limitations. Additionally, the paper examines the role of transfer learning and self-supervised learning in enhancing the efficiency of NLP models. Finally, potential future directions in NLP research, including multimodal learning and low-resource language adaptation, are discussed.