While mixed-language querying is ubiquitous in multilingual communities, the sensitivity of dense retrievers to such queries remains poorly understood. We present a ratio-controlled study on mMARCO that systematically evaluates retrieval performance by varying the mixing proportion of parallel query translations via embedding-level mixing -- constructing mixed queries as an interpolation of monolingual embeddings. Experiments with BGE-M3 demonstrate that an optimal mixing ratio outperforms the best monolingual endpoint in 88/105 cases. We uncover a distinct asymmetry driven by English dominance: mixing is uniformly beneficial when retrieving from non-English document indices, whereas indices containing English are best served by pure English queries. Furthermore, English acts as the strongest mixing partner for every non-English document language. Finally, when controlling for English dominance, mixing gains correlate negatively with typological distance. We conclude that language-mix sensitivity is structured and predictable, and we validate the robustness of these patterns across model families and scales.

翻译：尽管混合语言查询在多语言社区中普遍存在，但密集检索器对此类查询的敏感性仍鲜为人知。我们基于mMARCO开展了一项比例受控研究，通过嵌入层混合（将混合查询构建为单语言嵌入的插值）改变平行查询翻译的混合比例，系统评估检索性能。针对BGE-M3的实验表明，在88/105个案例中，最优混合比例的表现优于最佳单语言端点。我们发现英语主导性引发的显著不对称现象：当从非英语文档索引中检索时，混合查询普遍有益，而包含英语的索引则最适合使用纯英语查询。此外，对于每种非英语文档语言，英语都是最强的混合伙伴。最后，在控制英语主导性后，混合增益与类型距离呈负相关。我们得出结论：语言混合敏感性具有结构化与可预测性，并通过不同模型系列与规模验证了这些模式的稳健性。