Example Concept: Fine-tuning a large language model on a small dataset typically involves using transfer learning, where the model is pre-trained on a large corpus and then further trained (fine-tuned) on your specific dataset. Key strategies include freezing some layers to retain general knowledge, using techniques like dropout for regularization, and employing early stopping to prevent overfitting. Additionally, data augmentation and careful hyperparameter tuning can enhance performance.