Scaling Hybrid Batch Training with Multilingual Contrastive Objectives for Cross-Lingual Dense Retrieval

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 cross-lingual transferability of dense retrieval models across diverse language families in BEIR and XTREME-R benchmarks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.1/10.