International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064

Downloads: 0 | Views: 103

Review Papers | Computer Science | India | Volume 12 Issue 11, November 2023 | Rating: 5 / 10

Unveiling the Power of Pre - Trained Language Models in NLP Applications

Shrinath Pai [3]

Abstract: In recent years, pre - trained language models have ignited a revolution in the field of Natural Language Processing (NLP). These models, such as BERT, GPT - 3, and their variants, have demonstrated remarkable capabilities in a wide range of NLP applications. This paper explores the transformative impact of pre - trained language models in the realm of NLP. Our investigation begins by delving into the architecture and training processes behind these language models, shedding light on the mechanisms that enable them to grasp the intricacies of human language. We discuss how these models have surpassed traditional approaches by learning contextual information, making them adept at tasks like text classification, sentiment analysis, and language generation. The paper presents a comprehensive survey of NLP applications where pre - trained language models have excelled, including machine translation, question - answering systems, and Chabot. We examine case studies and real - world implementations that showcase the practicality of these models across various domains. Furthermore, we address the challenges and limitations associated with pre - trained language models, emphasizing issues related to model size, computational resources, and ethical considerations. We discuss ongoing research efforts aimed at mitigating these challenges and making NLP models more accessible. In the context of fine - tuning pre - trained models for specific tasks, we provide insights into best practices, transfer learning strategies, and techniques to achieve state - of - the - art results. We also discuss open - source resources and frameworks that facilitate the integration of pre - trained models into NLP pipelines. The paper concludes with a forward - looking perspective on the future of pre - trained language models in NLP. We explore potential research directions, including multilingual applications, domain - specific fine - tuning, and advancements in model interpretability. We emphasize the critical role of collaboration among researchers, practitioners, and the wider NLP community in harnessing the full potential of these language models. In summary, this paper unveils the power of pre - trained language models in NLP applications, showcasing their transformative impact, practical relevance, and potential for future innovations in the field. It serves as a guide for researchers, developers, and practitioners seeking to leverage the capabilities of these models to tackle complex language understanding and generation tasks.

Keywords: Pre - trained Model, Natural Language Processing, Sentiment Analysis, Machine Translation, Analysis

Edition: Volume 12 Issue 11, November 2023,

Pages: 1174 - 1177

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