Hybrid Batch Training for Zero-Shot Retrieval on Low-Resource Languages in the XQuAD 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: Does the hybrid batch training strategy proposed in this paper maintain or improve zero-shot retrieval accuracy on low-resource languages in the XQuAD benchmark compared to separate monolingual and cross-lingual fine-tuning?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10.