Example Concept: Fine-tuning a large language model on a small dataset involves several key steps: First, use data augmentation to artificially expand the dataset. Then, apply regularization techniques like dropout or weight decay to prevent the model from learning noise. Additionally, employ early stopping to monitor the model's performance on a validation set and halt training when performance starts to degrade, indicating potential overfitting.