Hybrid Batch Training for 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: Does the hybrid batch training strategy improve cross-lingual retrieval performance on MLDR when evaluated on low-resource languages compared to monolingual fine-tuning, as measured by mean reciprocal rank (MRR) on the MTOP benchmark?

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