Scaling Hybrid Batch Training for Zero-Shot Cross-Lingual Retrieval in Multilingual Language 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 effect of scaling the hybrid batch training strategy to larger multilingual language models (e.g., mT5, BLOOMZ) on zero-shot cross-lingual retrieval performance as measured by MRR@10 on the X-Wiki benchmark?

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