Hybrid Batch Training and Robustness in Multilingual Representation Alignment under Domain Shift

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 hybrid batch training affect the robustness of multilingual representation alignment under domain shift conditions in zero-shot cross-lingual retrieval?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.7/10.