@inproceedings{kim-etal-2025-milq,
    title = "{M}i{LQ}: Benchmarking {IR} Models for Bilingual Web Search with Mixed Language Queries",
    author = "Kim, Jonghwi  and
      Kang, Deokhyung  and
      Hwang, Seonjeong  and
      Kim, Yunsu  and
      Ok, Jungseul  and
      Lee, Gary",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1153/",
    pages = "22654--22670",
    ISBN = "979-8-89176-332-6",
    abstract = "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***, ***Mi***xed-***L***anguage ***Q***uery 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."
}