When Does Mixing Help? Analyzing Query Embedding Interpolation in Multilingual Dense Retrieval

Title:When Does Mixing Help? Analyzing Query Embedding Interpolation in Multilingual Dense Retrieval

Abstract: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.

Computation and Language (cs.CL)

arXiv:2606.13537 [cs.CL]

(or arXiv:2606.13537v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.13537 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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