@inproceedings{hong-etal-2026-query,
    title = "Query-Synergy: Leveraging High-Resource Languages for Improving Retrieval Performance Across Multiple Languages",
    author = "Hong, Seongtae  and
      Lee, Jungseob  and
      Moon, Hyeonseok  and
      Lee, Seungyoon  and
      Jang, Youngjoon  and
      Lim, Heuiseok",
    editor = "Huang, Kaiyu  and
      Mo, Fengran  and
      Chen, Pinzhen  and
      Jiang, Meng",
    booktitle = "Proceedings of the 1st Workshop on Multilinguality in the Era of Large Language Models ({M}e{LLM} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.mellm-1.4/",
    pages = "44--51",
    ISBN = "979-8-89176-430-9",
    abstract = "Multilingual embedding models often exhibit uneven representational quality, heavily favoring high-resource languages like English. However, conventional retrieval systems that rely exclusively on source-language queries fail to exploit the superior semantic expressiveness of these high-resource subspaces. To address this, we propose Query-Synergy, a training-free approach to improving retrieval performance using multilingual embeddings. Our method utilizes additional queries in English to complement source language queries and integrates similarity scores from both queries, effectively enhancing retrieval performance. We evaluate our approach across five languages (Arabic, Chinese, Greek, Thai, and Turkish) using four multilingual embedding models on two datasets. Our experiments show that this approach outperforms conventional source query retrieval methods, achieving superior nDCG scores across various configurations and translation settings. These results confirm that Query-Synergy is a simple yet effective method for retrieval across multiple languages."
}