26 May 23

If you are reading this then you have already probably heard of chatGPT and have even used it. Its human like responses when using it have taken the world by storm. However what if we were to look inside and see how it works?

In recent years, the field of natural language processing (NLP) has witnessed remarkable advancements, thanks to the advent of transformer-based models. Among them, the Generative Pretrained Transformer (GPT) technology has emerged as a groundbreaking approach, revolutionizing various applications in language generation, understanding, and translation. In this article, we will delve into the workings and structure of GPT, exploring the underlying mechanisms that have led to its success.

Transformer Architecture

At the heart of GPT lies the transformer architecture, which was introduced by Vaswani et al. in 2017. The transformer represents a departure from traditional recurrent neural networks (RNNs) by utilizing self-attention mechanisms to capture global dependencies between words in a sentence. It replaces sequential processing with parallel computation, significantly improving both training efficiency and the ability to model long-range dependencies.

Structure of GPT:

  1. Pretraining Phase: The GPT model undergoes a two-step training process. In the first phase, known as pretraining, the model is exposed to a large corpus of text data, such as books, articles, and web pages. During pretraining, the model learns to predict the next word in a sentence given the preceding context. This unsupervised learning task is called language modeling.To handle the vast amount of data, GPT utilizes a variant of the transformer known as the “Transformer Decoder.” The decoder consists of multiple layers, each comprising a self-attention mechanism and a feed-forward neural network. These layers enable the model to capture intricate patterns and relationships in the text.To further enhance the learning process, GPT uses a technique called “masked language modeling.” Randomly selected words in the input sentences are masked, and the model must predict the masked words based on the remaining context. This mechanism encourages the model to grasp contextual understanding rather than relying solely on superficial cues.
  2. Fine-Tuning Phase: After pretraining, the model enters the fine-tuning phase, where it is trained on specific downstream tasks. These tasks could include sentiment analysis, question-answering, text summarization, or machine translation, among others. During fine-tuning, the model is provided with labeled data related to the target task and learns to make predictions or generate appropriate responses.Fine-tuning typically involves adjusting the parameters of the pretrained model using supervised learning techniques. By leveraging the knowledge acquired during pretraining, GPT demonstrates superior performance on various NLP tasks, even with limited labeled data.
  3. Self-Attention Mechanism: Central to the transformer architecture and GPT’s success is the self-attention mechanism. Self-attention allows the model to weigh the importance of different words in a sentence based on their contextual relevance. It achieves this by computing attention scores, which determine the contribution of each word to the representation of other words in the sentence.The self-attention mechanism considers three distinct aspects: a. Query: The word under consideration, to which attention is applied. b. Key: The word that holds contextual information. c. Value: The representation of the key word that influences the query.By calculating attention scores between queries and keys, the model assigns weights to the values. The weighted values are then combined to form the contextual representation of the query word. This process allows GPT to capture relationships between words efficiently.

The Generative Pretrained Transformer (GPT) technology has redefined the landscape of natural language processing. Built upon the powerful transformer architecture, GPT leverages self-attention mechanisms to comprehend complex linguistic patterns and generate coherent text. Through a combination of pretraining and fine-tuning, GPT showcases remarkable.

OK, this doesn’t tell you all the finer details of how it works in detail but it provides a good insight at a fairly top level of the structure of chatGPT and how it does what it does.

chatGPT currently used GPT-3.5 which isn’t the latest version of GPT which is currently GPT-4. We will cover that in a different article.