Impact of Scaling Hybrid Batch Training with Multilingual Contrastive Learning on Zero-Shot Cross-Lingual Retrieval Performance

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: What is the impact of scaling the hybrid batch training strategy with multilingual contrastive learning objectives on the zero-shot cross-lingual retrieval performance of models like mBERT or XLM-R across diverse language families in the BEIR benchmark, measured by nDCG@10 and MRR scores?

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