Cross-Lingual Transfer in Multilingual Foundation Models: Mechanisms and Optimization

Abstract

Multilingual language models demonstrate remarkable ability to transfer capabilities across languages, performing tasks in low-resource languages after training primarily on high-resource data. We investigate the mechanisms enabling cross-lingual transfer through systematic analysis of representation spaces, attention patterns, and parameter sharing across 100+ languages in models from 300M to 175B parameters. Our findings reveal that successful transfer depends on three key factors: universal linguistic structures emerging in intermediate representations, language-agnostic task knowledge encoded in higher layers, and strategic vocabulary design enabling semantic alignment across scripts. We demonstrate that cross-lingual performance correlates strongly with typological similarity and shared script systems, but identify surprising transfer patterns suggesting models learn abstract linguistic primitives transcending surface forms. Through controlled interventions including language-specific adapter layers, vocabulary optimization, and targeted pre-training curricula, we achieve 40% improvement in zero-shot transfer for low-resource languages while maintaining high-resource performance. These insights enable more efficient multilingual model development and provide framework for understanding how neural networks represent linguistic knowledge abstractly.