Cross-Lingual Retrieval Performance of Block-Sparse FlashAttention in Multilingual Models on MLQA

Abstract

Abstract: Information retrieval across different languages is an increasingly important challenge in natural language processing. Recent approaches based on multilingual pre-trained language models have achieved remarkable success, yet they often optimize for either monolingual, cross-lingual, or multilingual retrieval performance at the expense of others. This paper proposes a novel hybrid batch training strategy to simultaneously improve zero-shot retrieval performance across monolingual, cross-lingual, and multilingual settings while mitigating language bias. The approach fine-tunes multilingual lang

Research Question

To what extent does the cross-lingual retrieval performance of Block-Sparse FlashAttention degrade when scaling to multilingual models with 10+ languages on the MLQA benchmark compared to local attention mechanisms?

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