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.

翻译：尽管双语使用者在网页搜索中频繁使用混合语言查询，但针对此类查询的信息检索（IR）研究仍然匮乏。为此，我们提出了MiLQ（混合语言查询测试集），这是首个公开的混合语言查询基准测试集，其数据被证实具有现实性且相对更受用户偏好。实验表明，多语言IR模型在MiLQ上表现中等，且在母语查询、英语查询和混合语言查询之间的性能表现不一致；实验结果同时提示，代码切换训练数据对于构建能够稳健处理此类查询的IR模型具有潜力。此外，分析表明，在查询中有意混合英语是双语使用者搜索英文文档的有效策略，我们将其归因于相较于纯母语查询，混合查询能带来更强的词元匹配效果。