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Compared to other language tasks, applying pre-trained language models (PLMs)for search ranking often requires more nuances and training signals. In thispaper, we identify and study the two mismatches between pre-training andranking fine-tuning: the training schema gap regarding the differences intraining objectives and model architectures, and the task knowledge gapconsidering the discrepancy between the knowledge needed in ranking and thatlearned during pre-training. To mitigate these gaps, we propose Pre-trained,Prompt-learned and Pre-finetuned Neural Ranker (P^3 Ranker). P^3 Rankerleverages prompt-based learning to convert the ranking task into a pre-traininglike schema and uses pre-finetuning to initialize the model on intermediatesupervised tasks. Experiments on MS MARCO and Robust04 show the superiorperformances of P^3 Ranker in few-shot ranking. Analyses reveal that P^3 Rankeris able to better accustom to the ranking task through prompt-based learningand retrieve necessary ranking-oriented knowledge gleaned in pre-finetuning,resulting in data-efficient PLM adaptation. Our code is available athttps://github.com/NEUIR/P3Ranker.

P^3 Ranker: Mitigating the Gaps between Pre-training and Ranking Fine-tuning with Prompt-based Learning and Pre-finetuning