Hybrid Batch Training Ratio Impact on Zero-Shot Retrieval in 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 ratio affect zero-shot retrieval performance on the XTREME-R benchmark when applied to multilingual models with varying parameter sizes (e.g., 7B vs. 30B)?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.