Query-aware routing for Cross-lingual performance gains in Encoders

Abstract:Multilingual encoders can exhibit reduced retrieval effectiveness when queries and relevant documents differ in language, despite strong same-language performance. We investigate whether Finnish and Swedish cross-lingual retrieval can improve while preserving an encoder's existing same-language performance and document index. We combine a query-only low-rank adapter, trained against frozen document embeddings, with deterministic routing based on query and index languages. Cross-language queries use the adapter, while same-language queries use the original encoder. SampoTron, our fine-tuned low-rank (LoRA) adapter alongwith the Nemotron-3-Embed-1B model, improves average retrieval quality across six English, Finnish, and Swedish directions from 0.241 to 0.291 in normalized discounted cumulative gain (nDCG) at rank ten, a 20.9% relative gain on a sampled financial benchmark. All six cross-lingual directions improve, and routing preserves the original same-language performance, including two full-corpus Finnish evaluations. The approach enables selective cross-language specialization with reusable document embedding vectors.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)

arXiv:2610.02875 [cs.CL]

(or arXiv:2610.02875v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.02875 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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