How are GPT models trained?

GPT models are trained using a method called language modeling. This method involves training a model to predict the next word or phrase in a sentence based on the context of the words or phrases that have come before. To do this, the model is given a large corpus of text and it will learn to identify patterns in the text. During training, the model is given a starting sentence and it will generate the next word or phrase based on what it has learned.

The training process can be improved by using different methods such as fine-tuning, which involves adjusting the pre-trained model’s parameters to better capture the patterns in a given dataset. Fine-tuning can help the model better capture nuances of language and produce more accurate results.

GPT models can also be trained using reinforcement learning, which involves providing rewards and penalties to the model to encourage it to generate more accurate sentences. This helps the model identify which words or phrases are more likely to appear in a sentence and further improve its accuracy.