Comparative Zero-Shot Retrieval Accuracy of Hybrid Batch Strategy Versus Specialized Fine-Tuning on Out-of-Domain Datasets

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 accuracy on out-of-domain datasets (e.g., BEIR, MTEB) compare between models trained with the proposed hybrid batch strategy and models fine-tuned separately for monolingual, cross-lingual, or multilingual objectives?

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