Study Reveals How Mixed-Language Queries Affect Multilingual Search Performance
Researchers conducted a systematic study on how mixing multiple languages in search queries affects retrieval performance in dense retrieval systems. The study found that optimal language mixing outperforms single-language queries in most cases, but English dominance creates asymmetric benefits depending on the document language. The findings could improve search systems for multilingual communities where code-switching is common.
A new study published on arXiv analyzes how dense retrieval systems handle mixed-language queries—a common scenario in multilingual communities. Using the mMARCO dataset and the BGE-M3 model, researchers systematically varied the proportion of parallel query translations combined at the embedding level. Results showed that optimal mixing ratios outperformed the best monolingual endpoint in 88 out of 105 test cases. However, the study uncovered a structured asymmetry: mixing was uniformly beneficial when searching non-English document collections, but indices containing English documents performed best with pure English queries. English emerged as the strongest mixing partner for every non-English language tested. When controlling for English dominance, mixing benefits correlated negatively with typological distance between languages. The patterns held consistently across different model families and scales, suggesting language-mix sensitivity is predictable and structured.
What different sources said
- arXiv cs.CLCenter
When Does Mixing Help? Analyzing Query Embedding Interpolation in Multilingual Dense Retrieval
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