Training ChatGPT: An Overview
Training OpenAI’s Chatbot GPT (Generative Pre-trained Transformer) involves a two-step process – pre-training and fine-tuning.
Pre-training
In this phase, the model is trained on a large corpus of text from the internet. The model isn’t aware of the specifics of the documents and doesn’t know the documents it was trained on. It’s akin to a student studying a vast library of books but not remembering specifics about each individual book.
Fine-tuning
After pre-training, GPT3 is then fine-tuned on a more specific dataset that’s generated with the help of human reviewers following certain guidelines provided by OpenAI.Following are the high-level steps to train ChatGPT:
- Preparation of the dataset for training and testing the model.
- Choosing the appropriate configuration for the model.
- Pre-training the model using the chosen configuration and dataset.
- Fine-tuning the model according to the intended application.
- Evaluating the performance of the model and making necessary adjustments.
The specifics of these processes are quite complex and proprietary to OpenAI. However, it’s important to note that the quality of your output greatly depends on the quality and structure of your input data during the training phase.
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