Optimizing Monolingual-to-Cross-Lingual Data Ratio in Multilingual Retrieval Benchmarks

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: To what extent does the monolingual-to-cross-lingual data ratio optimization generalize to other multilingual retrieval benchmarks (e.g., MLDoc, XOR) when using different multilingual language models (e.g., mBERT, XLM-R, mT5)?

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