How is ChatGPT trained?
ChatGPT is trained using a technique called unsupervised learning, which means that it is not provided with explicit labels or targets for the text it generates. Instead, it is trained on a large dataset of text, such as articles from Wikipedia, books, and web pages, and learns to generate text that is similar to the text it has seen during training.
The training process for ChatGPT starts with pre-processing the text data, which includes cleaning and normalizing the text, and tokenizing it into individual words or subwords. Once the text is pre-processed, it is fed into the model, and the model learns to predict the next word in a sequence based on the previous words.
During training, the model's parameters are adjusted to minimize the difference between the predicted word and the actual next word in the sequence. This process is repeated for a large number of training examples, which allows the model to learn the patterns and structure of the language.
After the model has been trained, it can be fine-tuned on a smaller dataset for a specific task or a specific domain, to improve its performance on that task.
The model is trained on powerful GPU's for several days to weeks depending on the size of the model. The biggest version of GPT-3, for instance, takes around 3-4 weeks to train on the latest GPU's.
