International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 06 | Jun 2023
p-ISSN: 2395-0072
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Exploring the Role of Transformers in NLP: From BERT to GPT-3 Abul Faiz Bangi1 1Student, Bachelor of Engineering, Pune Vidhyarthi Griha’s College of Engineering and Technology,
Maharashtra, India. ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - The paper "Exploring the Role of Transformers in
representations from extensive text data. BERT has achieved exceptional performance in diverse NLP tasks, such as text classification and question answering.
NLP: From BERT to GPT-3" provides an overview of the role of Transformers in NLP, with a focus on BERT and GPT-3. It covers topics such as the Role of Transformers in BERT, Transformer Encoder Architecture BERT, and Role of Transformers in GPT-3, Transformers in GPT-3 Architecture, Limitations of Transformers, Transformer Neural Network Design, and Pre-Training Process.
OpenAI introduced GPT-3 in 2020 as a language generation model. GPT-3 relies on a large pre-trained language model that can generate text in response to a given prompt. Its ability to generate highly coherent and human-like text has led many to consider it one of the most remarkable AI models to date.
The paper also discusses attention visualization and future directions for research, including developing more efficient models and integrating external knowledge sources. It is a valuable resource for researchers and practitioners in NLP, particularly the attention visualization section.
Transformer-based models excel at processing long-range dependencies in text, a challenge that traditional models such as RNNs struggle to handle as they process information sequentially. Transformers, however, can process all the words in a sentence simultaneously, capturing the relationships between all the words in the sentence.
Key Words: Transformers, NLP (Natural Language
Processing), BERT (Bidirectional Encoder Representations from Transformers), GPT-3 (Generative Pre-trained Transformer, Deep learning, Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs).
Transformers also excel at learning from vast amounts of unlabeled data. Pre-training a Transformer-based model on an extensive corpus of data allows it to learn general language representations, which can be fine-tuned for specific NLP tasks. The effectiveness of this approach is evident in the success of BERT and other Transformer-based models.
1. INTRODUCTION The field of Natural Language Processing (NLP) has historically been challenging due to the complexity and ambiguity of human language. Creating machines that can understand and process language in the same way humans do has been a difficult task. However, the emergence of deep learning in recent years has transformed the way NLP tasks are approached, and the development of Transformer-based models has taken NLP to new heights.
Despite their numerous advantages, Transformer-based models also possess some limitations. The most significant of these limitations is their high computational requirements. The training of a large Transformer-based model requires extensive computational resources, making it challenging for researchers with limited resources to work with these models. Furthermore, while Transformer-based models can handle long-range dependencies, they may struggle with certain syntactic structures, such as nested or recursive structures.
In 2017, Vaswani et al. introduced Transformers in their paper "Attention Is All You Need". These models utilize a distinct architecture that enables them to process information more efficiently than conventional models. Unlike traditional models that employ recurrent neural networks (RNNs) or convolutional neural networks (CNNs), Transformers use self-attention mechanisms to selectively focus on relevant information while disregarding noise.
1.1 BACKGROUND: The field of NLP has been a challenging one, largely due to the complexity and ambiguity of human language. However, recent developments in deep learning have transformed the way NLP tasks are approached, with the emergence of Transformer-based models such as BERT and GPT-3.
After their introduction, Transformers have been applied to numerous NLP tasks, including sentiment analysis, machine translation, and language generation. BERT and GPT-3 are two of the most popular Transformer-based models.
BERT, a bidirectional Transformer-based model, was introduced by Google in 2018. BERT employs a pre-trained language model and is capable of performing numerous NLP tasks. The primary innovation of BERT is its ability to
In 2018, Google introduced BERT, an acronym for Bidirectional Encoder Representations from Transformers. BERT employs a pre-training approach to acquire language
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