Cross-lingual to Monolingual Sample Ratio Effects on XLM-R Zero-shot Transfer Performance

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 cross-lingual to monolingual samples in a training batch on the zero-shot transfer performance of XLM-R models across the XTREME retrieval suite?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.2/10.