  Leaderboard systems allow researchers to objectively evaluate Natural
Language Processing (NLP) models and are typically used to identify models that
exhibit superior performance on a given task in a predetermined setting.
However, we argue that evaluation on a given test dataset is just one of many
performance indications of the model. In this paper, we claim leaderboard
competitions should also aim to identify models that exhibit the best
performance in a real-world setting. We highlight three issues with current
leaderboard systems: (1) the use of a single, static test set, (2) discrepancy
between testing and real-world application (3) the tendency for
leaderboard-centric competition to be biased towards the test set. As a
solution, we propose a new paradigm of leaderboard systems that addresses these
issues of current leaderboard system. Through this study, we hope to induce a
paradigm shift towards more real -world-centric leaderboard competitions.
