何时混合有帮助？分析多语言稠密检索中的查询嵌入插值
When Does Mixing Help? Analyzing Query Embedding Interpolation in Multilingual Dense Retrieval
AI总结：
通过嵌入级插值构造混合查询，系统研究多语言稠密检索对混合语言查询的敏感性，发现最优混合比在多数情况下优于单语言查询，且英语主导性导致不对称性。
摘要：
虽然混合语言查询在多语言社区中普遍存在，但稠密检索器对此类查询的敏感性仍知之甚少。我们在mMARCO上进行了比例控制研究，通过嵌入级混合——将混合查询构建为单语言嵌入的插值——系统地评估了改变平行查询翻译混合比例时的检索性能。使用BGE-M3的实验表明，在88/105个案例中，最优混合比优于最佳单语言端点。我们发现了由英语主导性驱动的明显不对称性：当从非英语文档索引中检索时，混合普遍有益，而包含英语的索引则最好使用纯英语查询。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，混合收益与类型学距离呈负相关。我们得出结论，语言混合敏感性是有结构且可预测的，并且我们验证了这些模式在模型家族和规模上的鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
