GPT-3 is a large-scale neural network model that uses a transformer-based architecture. The model is pre-trained on a massive corpus of text data using an unsupervised learning approach. During pre-training, the model is trained to predict the next word in a sequence of words, given the previous words in the sequence. This approach is known as a "language modeling" task, and it allows the model to learn patterns and relationships in language.
Once pre-trained, GPT-3 can be fine-tuned for a wide range of natural language processing tasks. During fine-tuning, the model's weights are adjusted to better suit the specific task at hand, using supervised learning techniques. For example, the model might be fine-tuned for sentiment analysis by training it on a labeled dataset of positive and negative reviews.
