Impact of Model Capacity on Monolingual and Cross-Lingual Retrieval Performance in Hybrid Batch-Trained 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: How does increasing model capacity (e.g., 3B to 7B parameters) in hybrid batch-trained models affect the trade-off between monolingual and cross-lingual retrieval performance on the XQuAD benchmark?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.