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ChatGPT (Chat Generative Pre-trained Transformer) works by using a deep neural network trained on a large dataset of text. The network is a type of language model known as a transformer, which is designed to understand the context and relationships between words in a sentence.
The model is pre-trained on a massive dataset, and then fine-tuned on a smaller dataset specific to the task it will be used for. During training, the model is fed large amounts of text and learns to predict the next word in a sequence.
When given a new input, ChatGPT uses this learned understanding of language to generate a response. It does this by predicting the probability of each word in the vocabulary being the next word in the sequence, given the input it has seen so far. The model then selects the word with the highest probability and uses that as its next word. This process is repeated until the model has generated a complete response.
The ChatGPT model uses a technique called attention mechanism which allows the model to focus on the most relevant parts of the input when generating a response. This enables the model to understand the context of the input and generate more coherent and relevant responses.