Scaling Zero-Shot Multilingual Retrieval Performance with Expanded Training Batch Languages

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 zero-shot multilingual retrieval performance scale when increasing the number of languages in the training batch beyond the NDQA benchmark, and what is the trade-off between performance and computational efficiency?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.6/10.