What is a key characteristic of models fine-tuned for specific tasks?

Fine-tuning models for specific tasks typically involves a process where pre-trained models are adapted using a smaller dataset that is highly relevant to the intended application. This targeted learning approach is advantageous because it allows the model to leverage the broad knowledge it gained during initial training, while also specializing its performance based on the nuances and specific requirements of the task at hand. By focusing on a smaller, task-specific dataset, the model efficiently learns to optimize for the desired outputs, leading to improved accuracy and relevance in its predictions or classifications.

In contrast, general data applicability and requirements for computational power or training time may not necessarily define fine-tuning. While a fine-tuned model might need computational resources that are already present from the base model, it doesn't inherently require more than what a general model might need. Similarly, increased training time is not a given; fine-tuning often allows for quicker and more efficient learning compared to training a model from scratch, especially when working with smaller datasets. Therefore, the targeted learning from smaller datasets characterizes the fine-tuning process.