What is the primary purpose of fine-tuning an LLM?

The primary purpose of fine-tuning a large language model (LLM) is to adapt a pre-trained model to specific tasks using relevant data. Fine-tuning involves taking a model that has already been trained on a broad dataset and further training it on a more specialized dataset that is relevant to the specific use case. This process allows the model to learn nuances and characteristics particular to the task it will perform, improving its performance for that specific application.

For instance, if you have a model that has been pre-trained on general language data, fine-tuning it with a dataset related to medical texts will help the model specialize in understanding and generating medical terminology and contexts. This targeted training is essential for achieving better results in tasks such as sentiment analysis, question answering, or chatbots tailored to particular industries.