Align Then Adapt: Label-Efficient Adapter Learning for Asymmetric Dense Retrieval

arXiv:2604.03403v2 Announce Type: replace-cross Abstract: Dense retrieval systems increasingly face an asymmetry between complex instruction-like queries and relatively simple, static document collections. While stronger embedders can better understand such queries, re-embedding large corpora or fine-tuning large models is often impractical. We propose Efficient Retrieval Adapter (ERA), a query-side adapter learning framework for re-index-free retrieval adaptation. ERA first aligns the embedding spaces of a strong query embedder and a lightweight document embedder using unlabeled corpus documents, and then adapts the aligned query representation with a small number of labeled query-document pairs. Across 126 MAIR retrieval tasks from six domains, ERA improves average nDCG@10 by up to 8.2 points in symmetric settings and by more than 12 points in asymmetric settings, while using substantially fewer labels than supervised adapter training. These results show that retrieval systems can benefit from stronger query understanding without updating backbone embedders or rebuilding document indexes.

Related

• KaLM-Reranker-V1: Fast but Not Late Interaction for Compressed Document Reranking
• VDAR-Router: Adaptive LLMs Routing via Verbalized Query Difficulty Analysis Retrieval
• MARA: A Multimodal Adaptive Retrieval-Augmented Framework for Document Question Answering

Source: arXiv cs.CL | 2026-08-27