Fine-tuning is the process of improving an existing, pretrained machine learning or AI model by continuing its training on a smaller, domain-specific dataset. This additional training helps the model adapt to a particular context, task, or problem. Fine-tuning allows the model to specialize in areas where its original general training may not have been sufficient, leading to better performance on specific tasks or in certain industries.
For example, a language model pretrained on a wide range of text can be fine-tuned with legal documents to improve its ability to understand legal jargon and answer questions related to law. Fine-tuning is particularly effective in scenarios where computational resources or labeled data are limited, as it leverages the knowledge already captured in the pretrained model and applies it to more specialized use cases.