SHIFTing Languages in Multilingual RAG

SHIFT is a no-training approach for multilingual retrieval-augmented generation that reduces language bias in embedding models by estimating and subtracting a language-specific vector offset, bringing documents and queries from different languages into a shared semantic space. Testing with the 384-dimensional multilingual-e5-small model on the XRAG news collection and additional benchmarks found that the method substantially improved cross-language Recall@10 and overall retrieval when relevant answers were frequently in languages other than the query, while reducing same-language recall, particularly for German. The offsets stabilized using only hundreds to thousands of translation pairs but varied somewhat by corpus, suggesting that domain-matched parallel data is preferable to large generic datasets. The shifted vectors performed nearly identically under Qdrant’s HNSW approximate index and exact search, and a full shift strength of alpha 1.0 worked best in the reported small-model experiments. SHIFT is therefore most suitable for systems limited to a single shared index with genuinely multilingual answer distributions, whereas collections dominated by same-language questions and answers may benefit more from language-specific retrieval or translation-based methods.

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