  Performance in natural language processing, and specifically for the
question-answer task, is typically measured by comparing a model\'s most
confident (primary) prediction to golden answers (the ground truth). We are
making the case that it is also useful to quantify how close a model came to
predicting a correct answer even for examples that failed. We define the Golden
Rank (GR) of an example as the rank of its most confident prediction that
exactly matches a ground truth, and show why such a match always exists. For
the 16 transformer models we analyzed, the majority of exactly matched golden
answers in secondary prediction space hover very close to the top rank. We
refer to secondary predictions as those ranking above 0 in descending
confidence probability order. We demonstrate how the GR can be used to classify
questions and visualize their spectrum of difficulty, from persistent near
successes to persistent extreme failures. We derive a new aggregate statistic
over entire test sets, named the Golden Rank Interpolated Median (GRIM) that
quantifies the proximity of failed predictions to the top choice made by the
model. To develop some intuition and explore the applicability of these metrics
we use the Stanford Question Answering Dataset (SQuAD-2) and a few popular
transformer models from the Hugging Face hub. We first demonstrate that the
GRIM is not directly correlated with the F1 and exact match (EM) scores. We
then calculate and visualize these scores for various transformer
architectures, probe their applicability in error analysis by clustering failed
predictions, and compare how they relate to other training diagnostics such as
the EM and F1 scores. We finally suggest various research goals, such as
broadening data collection for these metrics and their possible use in
adversarial training.
