How to compare the model's answer with the ground truth in a benchmark? What metric to use?

To compare the model's answer with the ground truth in a benchmark, the choice of metric depends on the type of task:

• For classification tasks, metrics like Accuracy, Precision, Recall, F1-score are often used.
• For regression tasks, Mean Squared Error (MSE), Mean Absolute Error (MAE) are common.
• For natural language processing tasks, BLEU, ROUGE, METEOR are used.

For example, if a model classifies texts into categories, F1-score balances precision and recall well, especially with imbalanced classes.

It is important to choose a metric that reflects business goals and the specifics of the task to ensure the evaluation is as relevant as possible.