# Boosting Data Utilization for Multilingual Dense Retrieval

arXiv: https://arxiv.org/abs/2509.09459 · HTML: https://mlsanity.com/paper/2509.09459/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2025.

## Built on by (most cited first, 3 of 3)

- [CroSearch-R1: Better Leveraging Cross-lingual Knowledge for Retrieval-Augmented Generation](https://mlsanity.com/paper/2604.25182/) (2026)
- [Disentangled Contrastive Learning for Zero-Shot Multilingual Dense Retrieval](https://mlsanity.com/paper/2608.02189/) (2026)
- [Adaptive Utilization of Low-Rank Adaptation via Conditioned Gating](https://mlsanity.com/paper/2610.05800/) (2026)

## Abstract (arXiv)

Multilingual dense retrieval aims to retrieve relevant documents across different languages based on a unified retriever model. The challenge lies in aligning representations of different languages in a shared vector space. The common practice is to fine-tune the dense retriever via contrastive learning, whose effectiveness highly relies on the quality of the negative sample and the efficacy of mini-batch data. Different from the existing studies that focus on developing sophisticated model architecture, we propose a method to boost data utilization for multilingual dense retrieval by obtaining high-quality hard negative samples and effective mini-batch data. The extensive experimental results on a multilingual retrieval benchmark, MIRACL, with 16 languages demonstrate the effectiveness of our method by outperforming several existing strong baselines.