Comparative Analysis of Hybrid Batch Training for Zero-Shot Retrieval Across Language Families on Xtreme-R

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: How does the hybrid batch training strategy compare to other monolingual, cross-lingual, and multilingual training approaches in terms of zero-shot retrieval accuracy on the Xtreme-R benchmark when applied to different language families?

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