arXiv:2606. 18801v1 Announce Type: cross Abstract: With the rapid expansion of massive multilingual corpora, Multilingual Information Retrieval (MLIR) has emerged as a critical technology for global information access.
By Youngjoon Jang, Seongtae Hong, Hyeonseok Moon, Heuiseok Lim
MIMO: Multilingual Information Retrieval via Monolingual Objectives proposes a two‑stage framework that first aligns a student model to a stable English semantic space using knowledge distillation, then jointly optimizes distillation and cross‑lingual contrastive learning to improve retrieval discrimination while preserving alignment. The approach addresses language clustering and the trade‑off between cross‑lingual alignment and embedding uniformity, outperforming existing cross‑lingual training baselines on both multilingual and multi‑monolingual benchmarks. MIMO also remains competitive with larger off‑the‑shelf models and its alignment‑uniformity analysis clarifies the distinct roles of the two loss components.
whyItMatters":"The study demonstrates a practical method to enhance multilingual information retrieval performance by balancing alignment and uniformity, which is crucial for real‑world search environments where queries and documents span multiple languages."
By Youngjoon Jang, Seongtae Hong, Heuiseok Lim
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
State-of-the-art retrieval models increasingly rely on closed training data, creating a reproducibility gap. We present an open end-to-end recipe for training retrieval models and study how English supervision transfers to multilingual retrieval through translate-train.
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
arXiv:2601. 04646v4 Announce Type: replace-cross Abstract: Large-scale multi-tenant retrieval systems generate extensive query logs but lack curated relevance labels for effective domain adaptation, resulting in substantial underutilized "dark data.
By Prateek Jain, Shabari S Nair, Ritesh Goru, Prakhar Agarwal, Ajay Yadav, Yoga Sri Varshan Varadharajan, Constantine Caramanis