Impact of Monolingual-Crosslingual Data Balance on XLM-R Generalization in BEIR 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 balance ratio between monolingual and cross-lingual data in the hybrid training approach affect the generalization of XLM-R models across different languages, as measured by accuracy on the BEIR multilingual benchmark?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.8/10.