Impact of Hybrid Batch Training Data Scaling on Monolingual and Cross-Lingual Retrieval Accuracy in XM3600 Versus Standard CLIP

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 scaling the size of the hybrid batch training data impact the trade-off between monolingual and cross-lingual retrieval accuracy on XM3600 compared to standard CLIP pre-training?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.7/10.