Impact of Multilingual Model Scaling on Zero-Shot Cross-Lingual Retrieval Performance in XTY

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 multilingual model size (e.g., from 125M to 1.5B parameters) on zero-shot cross-lingual retrieval performance (R@1, R@10) in the XTY benchmark when trained with the proposed hybrid batch strategy?

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