Hybrid Batch Training Ratios and Zero-Shot Retrieval Performance in Low-Resource 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: What is the effect of varying the ratio of monolingual to cross-lingual samples in hybrid batch training on the convergence speed and final MRR scores for low-resource languages in zero-shot retrieval tasks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.6/10.