Comparative Performance of Zero-Shot Cross-Lingual Retrieval Models Trained on Code-Switched Data Versus Multilingual Pre-trained

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 code-switched data compare to multilingual pre-trained models (e.g., mBERT, XLM-R) on standard multilingual benchmarks like MLQA or XNLI?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.6/10.