@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Query-Synergy: Leveraging High-Resource Languages for Improving Retrieval Performance Across Multiple Languages
%A Hong, Seongtae
%A Lee, Jungseob
%A Moon, Hyeonseok
%A Lee, Seungyoon
%A Jang, Youngjoon
%A Lim, Heuiseok
%Y Huang, Kaiyu
%Y Mo, Fengran
%Y Chen, Pinzhen
%Y Jiang, Meng
%S Proceedings of the 1st Workshop on Multilinguality in the Era of Large Language Models (MeLLM 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, United States
%@ 979-8-89176-430-9
%F hong-etal-2026-query
%X 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.
%U https://aclanthology.org/2026.mellm-1.4/
%P 44-51
Markdown (Informal)
[Query-Synergy: Leveraging High-Resource Languages for Improving Retrieval Performance Across Multiple Languages](https://aclanthology.org/2026.mellm-1.4/) (Hong et al., MeLLM 2026)
ACL