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This paper introduces a new testbed CLIFT (Clinical Shift) for the clinicaldomain Question-answering task. The testbed includes 7.5k high-quality questionanswering samples to provide a diverse and reliable benchmark. We performed acomprehensive experimental study and evaluated several QA deep-learning modelsunder the proposed testbed. Despite impressive results on the original testset, the performance degrades when applied to new test sets, which shows thedistribution shift. Our findings emphasize the need for and the potential forincreasing the robustness of clinical domain models under distributionalshifts. The testbed offers one way to track progress in that direction. It alsohighlights the necessity of adopting evaluation metrics that considerrobustness to natural distribution shifts. We plan to expand the corpus byadding more samples and model results. The full paper and the updated benchmarkare available at github.com/openlifescience-ai/clift

CLIFT: Analysing Natural Distribution Shift on Question Answering Models in Clinical Domain