Hybrid Batch Training vs. Contrastive Learning for Cross-Lingual Retrieval in 30B-Parameter Models

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 contrastive learning methods in maintaining monolingual accuracy while improving cross-lingual retrieval performance across different language families in 30B-parameter models?

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