What technique uses labeled examples to improve model performance on a specific task?

The technique that uses labeled examples to improve model performance on a specific task is instruction-based fine-tuning. This process involves taking a pre-trained model and further training it on a specific dataset that includes labeled examples relevant to a particular task. The goal of instruction-based fine-tuning is to optimize the model's parameters so that it can better understand and accurately perform specific tasks based on the labeled data provided.

In contrast, feature selection involves identifying and selecting a subset of relevant features from the dataset but does not inherently rely on labeled examples to improve model performance. Transfer learning is a broader concept where a model trained on one task is adapted to perform another task, which might include fine-tuning with specific data but doesn't specifically define the use of labeled examples for task improvement. Model validation refers to the process of assessing the performance of a model using validation data, but it also does not directly focus on enhancing the model through the use of labeled examples.

Through instruction-based fine-tuning, the model is able to leverage existing knowledge from prior training while specifically tailoring its performance to suit the nuances and requirements of the new task at hand.