Hybrid Batch Training Effects on Catastrophic Forgetting in Cross-Lingual Retrieval Across MIRACL Sequential Language Pairs

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 goal: How does hybrid batch training affect catastrophic forgetting rates in cross-lingual retrieval models when evaluated on the MIRACL benchmark across sequential language pairs?

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