How chatGPT works behind the scene: Part two
Hold on a sec! 🛑 Before diving into this part, make sure you’ve caught up with the first installment 🔙 by clicking on the link! Trust me, it’ll make the journey even smoother 🚀
Once the model has been pre-trained, it can be fine-tuned for specific tasks, such as answering questions or generating responses to prompts. During fine-tuning, the model is trained on a smaller, task-specific dataset to improve its performance further. This is a crucial step in building a chatbot because it allows the model to tailor its language generation capabilities to the specific needs of the chatbot.
🤖 For example, if the chatbot is being developed to answer questions, fine-tuning would involve training the model on a dataset of questions and answers 💬. This training process would help the model learn the relationships between questions and answers and how to generate appropriate responses to questions 🤔.
Fine-tuning typically involves using a smaller subset of the pre-training data 🧠, as well as a smaller model architecture 💻, to make the training process more efficient and to prevent overfitting task-specific data 🔍.
