Impact of Monolingual-to-Cross-Lingual Training Ratios on XNLI Retrieval Accuracy for Typologically Diverse 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 to cross-lingual training samples in hybrid batch training on retrieval accuracy for typologically diverse languages in the XNLI benchmark?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.1/10.