Impact of Model Size Scaling on Monolingual-Crosslingual Retrieval Trade-offs in Hybrid Batch Training

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 impact of model size scaling (e.g., 3B vs 13B parameters) on the trade-off between monolingual and cross-lingual retrieval performance in hybrid batch training?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.2/10.