How do we evaluate the performance of an NLP model beyond simple accuracy?

All answers to this question.

In imbalanced datasets, Accuracy is a trap. You need to look at the F1-Score, which is the harmonic mean of Precision and Recall. Precision tells you: "Of all the times the model predicted Negative, how many were actually Negative?" Recall tells you: "Of all the actual Negative samples, how many did the model catch?" In our 2024 project, we prioritized Recall because missing a "Negative" (angry) customer was more dangerous than mislabeling a "Positive" one. Always use a Confusion Matrix to see exactly where the model is getting confused. It’s the only way to get the full picture.

   Answered 2025-09-15 by Elizabeth Young

When dealing with generative models (LLMs) instead of classifiers, are metrics like BLEU or ROUGE still considered the gold standard?

   Answered 2025-10-05 by David Wilson

• David, BLEU and ROUGE are falling out of favor because they only check for word overlaps. Modern researchers are moving toward "LLM-as-a-judge" where a stronger model grades the output.

     Commented 2025-10-20 by Kevin Miller

For sentiment, I also recommend checking the "Area Under the Precision-Recall Curve" (AUPRC). It’s much more robust than the standard ROC-AUC for imbalanced data.

   Answered 2025-11-05 by Sandra Lewis

• I agree with Sandra. AUPRC really highlights how well the model handles the minority class, which in Nancy's case, is the most important part of the tool.

     Commented 2025-11-15 by Nancy Hall