Impact of Monolingual, Cross-Lingual, and Multilingual Sample Ratios on 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 impact of varying the ratio of monolingual, cross-lingual, and multilingual samples in hybrid batch training on the zero-shot retrieval performance of models evaluated on the XTYLO benchmark, particularly in low-resource language settings?

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