What is fine-tuning in the context of LLMs, and when is it typically applied?

Fine-tuning in the context of LLMs refers to the process of taking a pre-trained language model and continuing its training on a smaller, task-specific dataset. This is typically applied when you want to adapt a general-purpose model to perform better on a specific task, such as sentiment analysis, question answering, or domain-specific language generation. Fine-tuning allows the model to learn more nuanced patterns relevant to the specific application while leveraging the general knowledge acquired during pre-training, thereby enhancing performance on the intended task.