← Back to all news
arXiv Machine Learning By Anders Wikum, Nina Mishra, Amin Saberi, Tal Wagner

Learning Query Encoders Can Be Hard Even When Vector Retrieval Is Geometrically Easy

Read the original on arXiv Machine Learning →

The paper investigates the geometric capacity of vector retrieval systems, focusing on the maximum recall achievable with a fixed document index. It demonstrates that, on real-world benchmarks, single-vector query encoders often underperform relative to the index’s potential. The authors provide theoretical evidence that learning such encoders can be computationally hard, constructing a task where a simple neural network can achieve perfect recall while any statistical-query learner would need exponentially many queries to surpass random chance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Computation and Language
Aug 27

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

The paper introduces Efficient Retrieval Adapter (ERA), a query‑side adapter framework that enables dense retrieval systems to adapt to asymmetric query–document scenarios without re‑indexing. ERA first aligns the embedding spaces of a powerful query embedder and a lightweight document embedder using unlabeled documents, then fine‑tunes the aligned query representation with a small set of labeled query‑document pairs. In experiments on 126 MAIR retrieval tasks across six domains, ERA boosts average nDCG@10 by up to 8.2 points in symmetric settings and over 12 points in asymmetric settings while requiring far fewer labels than fully supervised adapter training.

By Seiji Maekawa, Moin Aminnaseri, Pouya Pezeshkpour, Estevam Hruschka
ragfine-tuning
More like this →
arXiv AI
Jun 17

Non-negative Elastic Net Decoding for Information Retrieval

arXiv:2606. 17910v1 Announce Type: cross Abstract: Dense retrieval has become the dominant paradigm in information retrieval, in which each document is scored against a query by the inner product of their vector embeddings, and the top-$k$ documents by score are retrieved for this query.

By Koki Okajima, Yasutoshi Ida, Tsukasa Yoshida, Yasuaki Nakamura
ragbenchmarks
More like this →
arXiv AI
Aug 19

DEPT: Document Embedding Preservation Tuning for Unified Query Expansion and Retrieval

The paper introduces DEPT, a method that trains a single decoder-only large language model to both expand queries and encode documents for retrieval. By preserving document embeddings close to their initial cached values while allowing gradients to flow through the generator, DEPT stabilizes retrieval targets and enables efficient index reuse and online hard‑negative mining. Experiments on the BEIR benchmark with Qwen3‑4B‑Instruct‑2507 and LLaMA‑3.2‑3B‑Instruct show that DEPT outperforms training‑free, independently trained, and staged unified baselines, with ablations confirming the benefits of preservation, whitening, end‑to‑end expansion training, and online negatives.

By Jingyuan Wang, Richong Zhang, Zhijie Nie, Mingxin Li, Yanzhao Zhang
llmsragbenchmarks
More like this →