Improving Korean-English Cross-Lingual Retrieval: A Data-Centric Study of Language Composition and Model Merging
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916026473512960 |
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| author | Jang, Youngjoon Son, Junyoung Lee, Taemin Hong, Seongtae Moon, Hyeonseok Lee, Seungyoon Matteson, Andrew Lim, Heuiseok |
| author_facet | Jang, Youngjoon Son, Junyoung Lee, Taemin Hong, Seongtae Moon, Hyeonseok Lee, Seungyoon Matteson, Andrew Lim, Heuiseok |
| contents | With the increasing utilization of multilingual text information, Cross-Lingual Information Retrieval (CLIR) has become a crucial research area. However, the impact of training data composition on both CLIR and Mono-Lingual Information Retrieval (IR) performance remains under-explored. To systematically investigate this data-centric aspect, we construct linguistically parallel Korean-English datasets and train retrieval models with various language combinations. Our experiments reveal that the language composition of training data significantly influences IR performance, exhibiting important inter-lingual correlations: CLIR performance improves with specific language pairs, while Mono-Lingual IR performance declines. Our work demonstrates that Model Merging can effectively mitigate this trade-off, achieving strong CLIR results while preserving Mono-Lingual IR capabilities. Our findings underscore the effects of linguistic configuration of training data on both CLIR and Mono-Lingual IR, and present Model Merging as a viable strategy to optimize performance across these tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08480 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving Korean-English Cross-Lingual Retrieval: A Data-Centric Study of Language Composition and Model Merging Jang, Youngjoon Son, Junyoung Lee, Taemin Hong, Seongtae Moon, Hyeonseok Lee, Seungyoon Matteson, Andrew Lim, Heuiseok Information Retrieval With the increasing utilization of multilingual text information, Cross-Lingual Information Retrieval (CLIR) has become a crucial research area. However, the impact of training data composition on both CLIR and Mono-Lingual Information Retrieval (IR) performance remains under-explored. To systematically investigate this data-centric aspect, we construct linguistically parallel Korean-English datasets and train retrieval models with various language combinations. Our experiments reveal that the language composition of training data significantly influences IR performance, exhibiting important inter-lingual correlations: CLIR performance improves with specific language pairs, while Mono-Lingual IR performance declines. Our work demonstrates that Model Merging can effectively mitigate this trade-off, achieving strong CLIR results while preserving Mono-Lingual IR capabilities. Our findings underscore the effects of linguistic configuration of training data on both CLIR and Mono-Lingual IR, and present Model Merging as a viable strategy to optimize performance across these tasks. |
| title | Improving Korean-English Cross-Lingual Retrieval: A Data-Centric Study of Language Composition and Model Merging |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2507.08480 |