Effect of Monolingual-to-Cross-Lingual Ratio on Zero-Shot Retrieval in XQuAD

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 batches on the convergence speed and final F1 scores for zero-shot retrieval in the XQuAD benchmark?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.9/10.