Hybrid Batch Training vs. Domain-Adaptive Fine-Tuning for Zero-Shot Cross-Lingual Retrieval on XQuAD

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 hybrid batch training strategy compare to domain-adaptive fine-tuning on the XQuAD benchmark in terms of zero-shot cross-lingual retrieval accuracy for low-resource languages? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.6/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 7.6/10.