  Knowledge of questions' difficulty level helps a teacher in several ways,
such as estimating students' potential quickly by asking carefully selected
questions and improving quality of examination by modifying trivial and hard
questions. Can we extract such benefits of instance difficulty in NLP? To this
end, we conduct Instance-Level Difficulty Analysis of Evaluation data (ILDAE)
in a large-scale setup of 23 datasets and demonstrate its five novel
applications: 1) conducting efficient-yet-accurate evaluations with fewer
instances saving computational cost and time, 2) improving quality of existing
evaluation datasets by repairing erroneous and trivial instances, 3) selecting
the best model based on application requirements, 4) analyzing dataset
characteristics for guiding future data creation, 5) estimating Out-of-Domain
performance reliably. Comprehensive experiments for these applications result
in several interesting findings, such as evaluation using just 5% instances
(selected via ILDAE) achieves as high as 0.93 Kendall correlation with
evaluation using complete dataset and computing weighted accuracy using
difficulty scores leads to 5.2% higher correlation with Out-of-Domain
performance. We release the difficulty scores and hope our analyses and
findings will bring more attention to this important yet understudied field of
leveraging instance difficulty in evaluations.
