Hybrid Batch Training for Zero-Shot Cross-Lingual Retrieval in Large Multilingual Models

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 the hybrid batch training strategy impact zero-shot cross-lingual retrieval performance when scaled to larger multilingual language models like M2M-100, evaluated on nDCG@10 and MRR across the BEIR benchmark for mid-resource languages?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.0/10.