ChatGPT was trained using a machine learning technique called transformer neural networks, specifically the GPT (Generative Pre-trained Transformer) architecture. The training process involved two steps: pre-training and fine-tuning. In pre-training, the model was trained on a large corpus of text from the internet, in order to learn grammar, facts about the world, and some level of reasoning abilities, like a language model. However, it’s worth noting that, the model doesn’t know specifics about which documents were in its training set or retrieve information from specific documents.
For the fine-tuning stage, the model was trained on a dataset generated with the help of human reviewers following guidelines provided by OpenAI. This dataset consisted of different examples of correct and incorrect ways to respond to user prompts. The reviewers reviewed and rated possible model outputs for a range of example inputs. The fine-tuning process helped the model understand how to respond to given prompts and accommodate a variety of conversational scenarios. So, for each output that the ChatGPT provides, it’s not copying from a specific document but generating a text based on patterns and information it learned during the training process.
