Hybrid Batch Training for Zero-Shot Cross-Lingual Retrieval in Low-Resource Languages

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 proposed in this paper compare to other multilingual pre-training techniques (e.g., mBERT, XLM-R) in terms of zero-shot cross-lingual retrieval accuracy on MIRACL's low-resource languages, when evaluated against standard benchmarks like MIRACL and XNLI?

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