Hybrid Batch Training Data Ratio Effects on Zero-Shot Cross-Lingual Retrieval Performance in Multilingual 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: What is the impact of varying the ratio of monolingual, cross-lingual, and multilingual data in hybrid batch training on the zero-shot cross-lingual retrieval performance of multilingual models, as measured by mAP on the BEIR benchmark?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10.