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Performance gaps in multilingual medical evaluations reveal that proprietary models outperform open-source ones, but translation quality can swing results dramatically.
Reranking documents by language coherence can dramatically improve retrieval performance, as shown by LAMAR's superior results across multiple languages.
LLMs can now perform inference without ever seeing raw text, opening the door to privacy-preserving applications without sacrificing performance.
Multilingual retrievers often prioritize irrelevant English documents over relevant foreign-language documents, even when the query is in that foreign language.
Low-resource languages can get a 15% boost in cross-lingual retrieval accuracy by using English as a Rosetta Stone during training.
Forget just mining hard negatives: the secret to better knowledge distillation for retrieval lies in matching the *entire* score distribution of your teacher model.
