Hybrid Batch Training Impact on Multilingual Retrieval Model Alignment with Human Judgments

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 hybrid batch training strategy affect the alignment of multilingual retrieval models with human judgments, as measured by the BEIR retrieval benchmark, when compared to models trained with monolingual or cross-lingual objectives separately?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.2/10.