Scaling Model Size and Trade-offs in Hybrid Batch Training for Monolingual and Cross-lingual Retrieval Performance

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: To what extent does scaling the model size affect the trade-off between monolingual and cross-lingual retrieval performance when using a hybrid batch training strategy, as measured by evaluation on benchmarks such as BEIR and MULTICORA?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.1/10.