GPT is trained using a large corpus of text data from the internet, books, articles, and other sources. Here is how GPT is trained to generate coherent and contextually relevant responses:
1. Unsupervised Learning:
GPT uses unsupervised learning, where it learns to predict the next word in a sentence. This helps it understand language structure and context.
2. Transformer Architecture:
GPT utilizes the Transformer architecture, which allows it to process and generate text efficiently by attending to different parts of the input text.
3. Fine-tuning:
After pre-training, GPT is fine-tuned on specific tasks or datasets to improve its performance in generating responses for particular contexts.
4. Context Window:
GPT considers a context window of previous words to generate responses that are coherent and contextually relevant.
5. Self-attention Mechanism:
GPT utilizes a self-attention mechanism to weigh the importance of different words in the input text, helping it generate meaningful responses.