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In any ranking system, the retrieval model outputs a single score for adocument based on its belief on how relevant it is to a given search query.While retrieval models have continued to improve with the introduction ofincreasingly complex architectures, few works have investigated a retrievalmodel's belief in the score beyond the scope of a single value. We argue thatcapturing the model's uncertainty with respect to its own scoring of a documentis a critical aspect of retrieval that allows for greater use of current modelsacross new document distributions, collections, or even improving effectivenessfor down-stream tasks. In this paper, we address this problem via an efficientBayesian framework for retrieval models which captures the model's belief inthe relevance score through a stochastic process while adding only negligiblecomputational overhead. We evaluate this belief via a ranking based calibrationmetric showing that our approximate Bayesian framework significantly improves aretrieval model's ranking effectiveness through a risk aware reranking as wellas its confidence calibration. Lastly, we demonstrate that this additionaluncertainty information is actionable and reliable on down-stream tasksrepresented via cutoff prediction.

Not All Relevance Scores are Equal: Efficient Uncertainty and Calibration Modeling for Deep Retrieval Models