How does GPT work?

Generative Pre-trained Transformer (GPT) is a type of language model developed by OpenAI. It is based on the Transformer architecture introduced in the paper “Attention Is All You Need” by Vaswani et al. The main idea behind GPT is to pre-train a deep neural network on a large corpus of text data, and then fine-tune it for specific NLP tasks such as text classification, translation, or summarization.

GPT uses a transformer-based architecture that is trained on a large corpus of text data to predict the next word in a sequence, given the previous words as input. The model is trained in a unsupervised manner, meaning that it is trained on the text data without explicit labels. Instead, it tries to predict the next word in a sentence given the previous words, and updates its weights to maximize the likelihood of this prediction. Once the pre-training is completed, the model can be fine-tuned for specific NLP tasks by adding task-specific layers to the pre-trained model and training it on task-specific data.

GPT's success lies in its ability to capture the context and meaning of words in a sentence, as well as the relationships between words, through its attention mechanism. The attention mechanism allows the model to focus on different parts of the input sentence and weigh their importance in determining the final prediction. The transformer architecture and attention mechanism also allow the model to efficiently parallelize the computation, leading to fast training and inference times even on large datasets. Overall, GPT's pre-training approach, combined with its attention-based architecture, has resulted in state-of-the-art performance on a wide range of NLP tasks.