UnIte: Uncertainty-based Iterative Document Sampling for Domain Adaptation in Information Retrieval

arXiv:2604.25142v1 Announce Type: cross Abstract: Unsupervised domain adaptation generalizes neural retrievers to an unseen domain by generating pseudo queries on target domain documents. The quality and efficiency of this adaptation critically depend on which documents are selected for pseudo query generation. The existing document sampling method focuses on diversity but fails to capture model uncertainty. In contrast, we propose **Un**certainty-based **Ite**rative Document Sampling (UnIte) ad