Performance Variation of Zero-Shot Retrieval in Multilingual Models with Hybrid Batch Strategy

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 zero-shot retrieval performance (nDCG@10) on the BEIR benchmark vary when applying the hybrid batch strategy to multilingual models of different architectures (e.g., transformer vs. sparse-attention models) compared to their monolingual counterparts?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.