Synergistic Training for Robust Monolingual and Cross-Lingual Retrieval in Low-Resource Settings

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 synergistic training strategy for monolingual and cross-lingual retrieval maintain robustness against language imbalance in low-resource settings compared to standard multilingual contrastive learning on the BEIRS multilingual subset?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.0/10.