Zero-, one-, and few-shot learning – typical of transformers models
In the previous chapters, we mentioned how OpenAI models, and hence also ChatGPT, come in a pre-trained format. They have been trained on a huge amount of data and have had their (billions of) parameters configured accordingly.
However, this doesn’t mean that those models can’t learn anymore. In Chapter 2, we saw that one way to customize an OpenAI model and make it more capable of addressing specific tasks is by fine-tuning.
Definition
Fine-tuning is the process of adapting a pre-trained model to a new task. In fine-tuning, the parameters of the pre-trained model are altered, either by adjusting the existing parameters or by adding new parameters so that they fit the data for the new task. This is done by training the model on a smaller labeled dataset that is specific to the new task. The key idea behind fine-tuning is to leverage the knowledge learned from the pre-trained model and...
