Hybrid Batch Training Ratio Impact on Zero-Shot Cross-Lingual Retrieval in CLIP and ALBEF 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 the proposed hybrid batch training ratio affect the zero-shot cross-lingual retrieval performance of CLIP and ALBEF models when evaluated on domain-specific datasets beyond XCOCO and XFlickr30k, such as Wikipedia or news articles, compared to baseline strategies?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10.