Dense retrievers powered by pretrained embeddings are widely used for document retrieval but struggle in specialized domains due to the mismatches between the training and target domain distributions. Domain adaptation typically requires costly annotation and retraining of query-document pairs. In this work, we revisit an overlooked alternative: applying PCA to domain embeddings to derive lower-dimensional representations that preserve domain-relevant features while discarding non-discriminative components. Though traditionally used for efficiency, we demonstrate that this simple embedding compression can effectively improve retrieval performance. Evaluated across 9 retrievers and 14 MTEB datasets, PCA applied solely to query embeddings improves NDCG@10 in 75.4% of model-dataset pairs, offering a simple and lightweight method for domain adaptation.

翻译：基于预训练嵌入的密集检索器广泛用于文档检索，但由于训练领域与目标领域分布之间的不匹配，在专业领域表现不佳。领域自适应通常需要昂贵的标注和查询-文档对的重新训练。本文重探了一个被忽视的替代方案：对领域嵌入应用主成分分析，以提取保留领域相关特征的低维表示，同时丢弃非判别性成分。尽管主成分分析传统上用于提升效率，我们证明这种简单的嵌入压缩能有效改善检索性能。在9个检索器和14个MTEB数据集上的评估表明，仅对查询嵌入应用主成分分析可在75.4%的模型-数据集对中提升NDCG@10指标，为领域自适应提供了一种简单且轻量的方法。