SHIFTing Languages in Multilingual RAG - Qdrant

This post introduces and evaluates the SHIFT method for mitigating language bias in multilingual RAG systems. It demonstrates that the SHIFT method, by treating languages as offsets, can significantly improve cross-language retrieval recall without requiring additional training or translation pipelines. The post provides empirical evidence using the `multilingual-e5-small` model and the XRAG dataset, showing improvements in recall@10 for both same-language and cross-language scenarios. It also details findings on offset convergence, dataset dependency, and the impact of SHIFTing vectors on Qdrant's HNSW index.