Impact of Hybrid Batch Training on Monolingual, Cross-Lingual, and Multilingual Retrieval Trade-offs in Scaled XQuAD 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 strategy affect the trade-off between monolingual, cross-lingual, and multilingual retrieval performance in XQuAD when scaling model size, measured by mean reciprocal rank (MRR) across different language pairs?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.0/10.