ChatGPT is powered by a transformer-based model, which processes text data in a hierarchical and parallel manner. Here’s how ChatGPT works:
1. Pre-training:
ChatGPT is pre-trained on a diverse corpus of text data to learn language patterns, grammar rules, and context. This step helps the model understand the nuances of human language.
2. Fine-tuning:
After pre-training, ChatGPT can be fine-tuned on specific datasets or tasks to improve performance. Fine-tuning adapts the model to a particular domain or application.
3. Inference:
During inference, ChatGPT takes an input text prompt and generates a response using the learned patterns and context. The model predicts the most probable next words based on the input sequence.
4. Response Generation:
ChatGPT generates responses by sampling from the probability distribution of the next word. The model prioritizes words that are more likely to occur in a given context, resulting in coherent and contextually relevant responses.