Performance comparison of zero-shot cross-lingual retrieval models on MIRACL benchmark

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 performance of zero-shot cross-lingual retrieval models trained on artificially code-switched data compare to multilingual BERT or XLM-R when evaluated on the MIRACL benchmark across diverse low-resource languages?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.9/10.