Synergistic Hybrid Batch Training Effects on MLQA Monolingual Retrieval Across Model Scales

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: Does the synergistic hybrid batch training strategy degrade monolingual retrieval performance on MLQA when optimizing for cross-lingual transfer across different model scales?

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