Comparative Effect of Hybrid Batch Training and Monolingual Fine-Tuning on Zero-Shot Cross-Lingual Retrieval Generalization in

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: What is the comparative effect of the proposed hybrid batch training versus monolingual fine-tuning on the generalization gap observed in zero-shot cross-lingual retrieval tasks within the XTYLE benchmark?

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