MiLQ: Benchmarking IR Models for Bilingual Web Search with Mixed Language Queries
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866909855370969088 |
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| author | Kim, Jonghwi Kang, Deokhyung Hwang, Seonjeong Kim, Yunsu Ok, Jungseul Lee, Gary |
| author_facet | Kim, Jonghwi Kang, Deokhyung Hwang, Seonjeong Kim, Yunsu Ok, Jungseul Lee, Gary |
| contents | Despite bilingual speakers frequently using mixed-language queries in web searches, Information Retrieval (IR) research on them remains scarce. To address this, we introduce MiLQ, Mixed-Language Query test set, the first public benchmark of mixed-language queries, qualified as realistic and relatively preferred. Experiments show that multilingual IR models perform moderately on MiLQ and inconsistently across native, English, and mixed-language queries, also suggesting code-switched training data's potential for robust IR models handling such queries. Meanwhile, intentional English mixing in queries proves an effective strategy for bilinguals searching English documents, which our analysis attributes to enhanced token matching compared to native queries. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_16631 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MiLQ: Benchmarking IR Models for Bilingual Web Search with Mixed Language Queries Kim, Jonghwi Kang, Deokhyung Hwang, Seonjeong Kim, Yunsu Ok, Jungseul Lee, Gary Information Retrieval Computation and Language Despite bilingual speakers frequently using mixed-language queries in web searches, Information Retrieval (IR) research on them remains scarce. To address this, we introduce MiLQ, Mixed-Language Query test set, the first public benchmark of mixed-language queries, qualified as realistic and relatively preferred. Experiments show that multilingual IR models perform moderately on MiLQ and inconsistently across native, English, and mixed-language queries, also suggesting code-switched training data's potential for robust IR models handling such queries. Meanwhile, intentional English mixing in queries proves an effective strategy for bilinguals searching English documents, which our analysis attributes to enhanced token matching compared to native queries. |
| title | MiLQ: Benchmarking IR Models for Bilingual Web Search with Mixed Language Queries |
| topic | Information Retrieval Computation and Language |
| url | https://arxiv.org/abs/2505.16631 |