Artificial Intelligence

Over the past decade in the field of Natural Language Processing (NLP), the Generative Pre-trained Transformer (GPT) has undoubtedly been one of the most iconic technologies. GPT has not only redefined the approach to language modeling but also sparked a revolution centered around pre-training, leading to the rise of general-purpose language models. This article begins with an overview of the GPT architecture and delves into the design principles and technological evolution from GPT-1 to GPT-3.

Bidirectional Encoder Representations from Transformers (BERT) is a pre-training technology for natural language processing proposed by Google AI in 2018. BERT significantly advances the state of natural language processing by providing a deeper contextual understanding of language.

Transformer model was introduced by a team at Google Brain in 2017 and is a deep learning architecture that uses an attention mechanism. It solves significant challenges associated with traditional sequence namely capturing long-range dependencies and enabling more parallelizable computations.

Attention mechanisms is a method in deep learning that lets a model focus on the most relevant parts of its input when producing each piece of its output. Unlike traditional sequence models that often struggle with longer inputs, attention allows models to dynamically focus on different parts of the input sequence when generating each part of the output sequence.

Sequence to Sequence (Seq2Seq) model is a neural network architecture that maps one sequence to another. It has revolutionized the field of Natural Language Processing (NLP), significantly enhancing the performance of tasks such as translation, text summarization, and chatbots. This article will dive deeply into the principles behind the Seq2Seq model.