Cross-lingual Transfer Learning Performance Comparison in Monolingual and Multilingual Language Models on XTREME-R Benchmark

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 performance of cross-lingual transfer learning compare between monolingual and multilingual pre-trained language models when evaluated on the XTREME-R benchmark using F1-score as the metric?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.6/10.