Year: 2026
Type: conference-paper
Abstract: Dense retrieval represents queries and documents as high-dimensional embeddings, but these representations can be redundant at the query level: for a given information need, only a subset of dimensions is consistently helpful for ranking.Prior work addresses this via pseudo-relevance feedback (PRF) based dimension importance estimation, which can produce query-aware masks without labeled data but often relies on noisy pseudo signals and heuristic test-time procedures.In contrast, supervised adapter methods leverage relevance labels to improve embedding quality, yet they learn global transformations shared across queries and do not explicitly model query-aware dimension importance.We propose a Query-Aware Adaptive Dimension Selection framework that learns to predict per-dimension importance directly from query embedding.We first construct oracle dimension importance distributions over embedding dimensions using supervised relevance labels, and then train a predictor to map a query embedding to these label-distilled importance scores.At inference, the predictor selects a query-aware subset of dimensions for similarity computation based solely on the query embedding, without pseudorelevance feedback.Experiments across multiple dense retrievers and benchmarks show that our learned dimension selector improves retrieval effectiveness over the full-dimensional baseline as well as PRF-based masking and supervised adapter baselines.
Authors: Zhanyu Wu, Richong Zhang, Zhijie Nie
Institution: Beihang University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Award: G3865857761
OpenAlex ID: W7166825815