What is ChatGPT?

GPT stands for generative pretrained transformer.

Generative

The "G" in GPT stands for "Generative". This refers to the model's ability to create or "generate" new content. In the context of GPT, this means that the model is able to write new text based on the information and patterns it has learned during training. So, when you ask it something or request a text, GPT can independently come up with answers or content that fits what you have asked.

Training data

The "P" stands for pre-trained. ChatGPT, as developed by OpenAI, is trained using a large dataset consisting of various sources such as books, websites, news articles and other forms of written text. The exact composition of the dataset is not publicly specified, but it covers a wide range of topics and genres to give the model versatility and understanding of different contexts.

The training data for such models are intended to encompass the broadest possible picture of human language, including general knowledge and information on a variety of topics, from science to art and from technology to everyday conversation.

OpenAI has also implemented mechanisms to prevent the model from learning from personal data or biased information, and the data is regularly evaluated and updated to improve the model's performance and accuracy.

Transformer

The "transformer" in ChatGPT is a type of architecture for deep neural networksthat is particularly effective for processing language. The uniqueness of transformers is their ability to take into account the full context of a text, unlike previous models that processed text sequentially.

Here is a simple explanation of how a transformer works:

  1. Attention (Attention):The key idea of the transformer is the "attention" mechanism that determines what the model should pay attention to in a sentence. Instead of analysing each sentence word by word, the transformer can look at the whole and determine relationships between words at each point in the sentence. This helps the model better understand how words affect each other.

  2. Parallel Processing: Unlike earlier models such as recurrent neural networks (RNNs), which process text sequentially, transformers can process the entire text simultaneously. This makes them much faster and more efficient, especially when working with long texts.

    Imagine you are reading a story. Older computing technologies, such as RNNs, read the story word by word, just as humans do. But transformers, used in ChatGPT, can see the whole story at once. This is as if they glance at the whole page at once, allowing them to quickly understand what it says. This allows them to work faster, especially with long texts, as they do not have to wait for each word to be read one by one.

  3. Layers of transformers:A transformer consists of multiple layers of these attention mechanisms and other neural network components. Each layer transforms the input step by step and builds an increasingly complex understanding of the text.

    The "layers" in a transformer model play a role both during training the model and when the model writes a text in response to a question.

    During training:Layers learn to recognise complex patterns and relationships in text. They are trained with large amounts of text and learn step by step to understand increasingly complex aspects of language. This includes learning grammar, word meanings, sentence structure, and even style and context.

    During use:When you ask a question, the model uses these trained layers to analyse the question and generate an appropriate answer. The layers work together to combine all learned information and produce a coherent, relevant and well-formulated answer. In doing so, they use the same mechanisms they learned during training to "understand" the text and then "write" new text based on it.

    So, whether understanding a text during training or writing a text when answering a question, the layers in the transformer model are actively involved in both processes, using what they have learned to deliver meaningful language output. 

    This ability to understand context broadly and deeply makes transformers very powerful for all kinds of tasks in natural language processing, such as translation, summarisation and, as in ChatGPT, text generation.
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