Fine-tuning

Fine-tuning is the process of updating the parameters of a pre-trained model to improve its performance on a specific task. It can be done for a variety of tasks, such as machine translation, text summarization, and question answering.

To fine-tune an LLM, you need to provide it with a dataset of labeled examples for the specific task. The LLM will then learn to update its parameters in order to minimize the loss function on the labeled data.

Fine-tuning can be a very effective way to improve the performance of an LLM on a specific task. However, it is important to note that fine-tuning can also lead to overfitting, which is when the LLM learns the training data too well and is unable to generalize to new data.