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arXiv · 2509.09459

Boosting Data Utilization for Multilingual Dense Retrieval

Chao Huang·Fengran Mo·Yufeng Chen·Changhao Guan·Zhenrui Yue·Xinyu Wang·Jinan Xu·Kaiyu Huang
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Abstract

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.

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Chao Huang, Fengran Mo, Yufeng Chen, Changhao Guan, Zhenrui Yue, Xinyu Wang, Jinan Xu, Kaiyu Huang. 2025-09-11. Boosting Data Utilization for Multilingual Dense Retrieval. https://arxiv.org/abs/2509.09459

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