Example Concept: Transfer learning for LLMs typically starts with a model pre-trained on a vast corpus of text, capturing general language patterns and knowledge. This pre-trained model is then fine-tuned on a smaller, task-specific dataset, allowing it to adapt its understanding to the nuances of the new task. The fine-tuning process involves adjusting the model's weights slightly, which is more efficient than training from scratch and requires less data.