Is there a big difference between fine-tuning and retraining a model?

Fine-tuning adapts a pre-existing model to a specific task using a relatively small dataset, whereas retraining involves building a new model architecture from scratch using extensive, often task-specific data. Both methods achieve model updates but differ significantly in approach and resource needs.

Fine-tuning modifies the weights of an established model (like BERT or GPT) trained on vast general data. It requires far less data and computing power than retraining, focuses primarily on the later layers of the neural network, and relies heavily on transfer learning. Retraining, also known as training from scratch, develops a completely new model; it demands massive, relevant datasets and substantial computational resources to learn fundamental features and patterns independently of any existing pre-trained model.

Fine-tuning is highly efficient for quickly specializing a model to a related, narrow domain like sentiment analysis for customer reviews. Retraining is necessary when the existing models' architectures or fundamental knowledge are inadequate for the novel task, such as creating a unique model for a previously unexplored data type, offering potentially better performance at great cost.