Performance Comparison of Hybrid Batch Training and Multitask Fine-Tuning for Zero-Shot Cross-Lingual Retrieval in Low-Resource

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's performance on zero-shot cross-lingual retrieval for low-resource Niger-Congo languages compare to multitask fine-tuning approaches that combine monolingual and cross-lingual objectives, as measured by MRR and NDCG scores on MIRACL and BEIR benchmarks? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.0/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 8.0/10.