Effectiveness of Artificial Code-Switching Training for Zero-Shot Cross-Lingual Retrieval on MIRACL

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 training on artificially code-switched data improve zero-shot cross-lingual retrieval performance on the MIRACL benchmark more effectively than multilingual contrastive learning objectives?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.4/10.