Scaling Hybrid Batch Training for Zero-Shot Cross-Lingual Retrieval in Low-Resource Niger-Congo Languages

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 scaling of the hybrid batch training strategy with increasing dataset size affect zero-shot cross-lingual retrieval performance on the MIRACL benchmark for low-resource Niger-Congo languages compared to contrastive learning methods? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.2/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 8.2/10.