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Pre-trained language models have become a crucial part of ranking systems andachieved very impressive effects recently. To maintain high performance whilekeeping efficient computations, knowledge distillation is widely used. In thispaper, we focus on two key questions in knowledge distillation for rankingmodels: 1) how to ensemble knowledge from multi-teacher; 2) how to utilize thelabel information of data in the distillation process. We propose a unifiedalgorithm called Pairwise Iterative Logits Ensemble (PILE) to tackle these twoquestions simultaneously. PILE ensembles multi-teacher logits supervised bylabel information in an iterative way and achieved competitive performance inboth offline and online experiments. The proposed method has been deployed in areal-world commercial search system.

PILE: Pairwise Iterative Logits Ensemble for Multi-Teacher Labeled Distillation