How does ChatGPT work?
ChatGPT works by using a neural network architecture known as the Transformer. The model is trained on a large corpus of text data, and it learns to generate text that is similar to the text it was trained on. The goal of the model is to generate text that is coherent and grammatically correct, and that follows the context and style of the input text.
The model takes an input text, such as a question or a prompt, and generates a response in the form of text. The input text is first tokenized, or broken down into individual words or phrases, and then passed through the model. The model uses this input text to generate a probability distribution for each word in the response, and then selects the word with the highest probability as the next word in the response. This process is repeated until the model generates a complete response.
The model uses a technique called attention mechanism, which allows it to focus on specific parts of the input text when generating the response. This allows the model to understand the context of the input text and generate a response that is relevant to the input text.
It is important to note that ChatGPT is a statistical model and it is not able to understand the meaning of the text but it is able to generate text that resembles human language. And the more data it is trained on, the more it can resemble human language and be able to respond better to different types of prompts and questions.