In the QA task, model relevance is assessed using F1 score and mean average error, comparing predicted outputs to ground truths. These metrics are computed via linear algebraic operations. Interpretability is gauged by examining attention weights, where the model's focus on input tokens during predictions is analyzed. By calculating the entropy of these weights, insights into the model’s decision-making process are obtained, highlighting areas of concentration and allowing for better understanding of its interpretability.
In the QA task, relevance is assessed by comparing model predictions to the ground truth, utilizing metrics like the F1 score and mean average error.
Accuracy is determined through metrics such as mean average error, which represents average absolute differences between predicted outputs and ground truth, providing a quantitative measure.
Interpretability was assessed by analyzing attention weights from the model during predictions, allowing for evaluations of how the model focuses on parts of the input.
Calculating the entropy of attention weights assists in quantifying the interpretability of the model, revealing how concentrated or dispersed the model’s focus is.
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