ChatGPT, developed by OpenAI, is a powerful language model that uses machine learning to respond to prompts and generate human-like text. The training process of ChatGPT involves two major steps: pre-training and fine-tuning.
In the pre-training phase, models are trained on a large corpus of text from the internet. However, ChatGPT doesn't know specifics about which documents were part of the training set. It doesn't access any personal data unless it's explicitly provided in the conversation. This process helps the model learn grammar, facts about the world, and some amount of reasoning ability.
After pre-training, the model undergoes a fine-tuning process. Here, it's trained on a narrower dataset generated with the help of human reviewers. These reviewers follow guidelines provided by OpenAI to review and rate possible model outputs for a range of example inputs. The model generalizes from this reviewer feedback to respond to a wide array of inputs from users.
The training process of ChatGPT is a continuous one. OpenAI maintains a strong feedback loop with reviewers, involving weekly meetings to address questions and provide clarifications on the guidelines. This iterative feedback process enables the model to improve over time.
Understanding the training process of ChatGPT can help us appreciate the complex mechanisms that enable it to generate such creative and contextually accurate responses.