Fine-tuning Hybrid Multilingual Models for Cross-Lingual Zero-Shot Retrieval Performance

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: What is the impact of fine-tuning the hybrid batch-trained multilingual model on domain-specific datasets (e.g., legal or medical corpora) on its zero-shot retrieval performance (nDCG@10) for cross-lingual queries, compared to standard training, as evaluated on the XBEIR benchmark?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.7/10.