Fine-tuning in information retrieval systems using pre-trained language models (PLM-based IR) requires learning query representations and query-document relations, in addition to downstream task-specific learning. This study introduces coarse-tuning as an intermediate learning stage that bridges pre-training and fine-tuning. By learning query representations and query-document relations in coarse-tuning, we aim to reduce the load of fine-tuning and improve the learning effect of downstream IR tasks. We propose Query-Document Pair Prediction (QDPP) for coarse-tuning, which predicts the appropriateness of query-document pairs. Evaluation experiments show that the proposed method significantly improves MRR and/or nDCG@5 in four ad-hoc document retrieval datasets. Furthermore, the results of the query prediction task suggested that coarse-tuning facilitated learning of query representation and query-document relations.

翻译：在信息检索系统中，使用预训练语言模型的微调（PLM-based IR）除了需要学习下游任务特定知识外，还需学习查询表示及查询-文档关系。本研究引入粗调作为连接预训练与微调的中间学习阶段。通过在粗调阶段学习查询表示与查询-文档关系，旨在减轻微调负担并提升下游IR任务的学习效果。我们提出用于粗调的查询-文档对预测（QDPP）方法，该模型可预测查询-文档对的匹配程度。评估实验表明，所提方法在四个即席文档检索数据集上显著提升了MRR和/或nDCG@5指标。此外，查询预测任务的结果表明，粗调促进了查询表示与查询-文档关系的有效学习。