A classifier prompt repeatedly confuses two labels. Which change is the most targeted few…

A classifier prompt repeatedly confuses two labels. Which change is the most targeted few-shot intervention?

1. Add representative, correctly labeled examples near the decision boundary and keep their format consistent
2. Add many unrelated examples so the prompt becomes longer
3. Remove the label definitions and let the model infer a new taxonomy