Hybrid Batch Training Effects on Multilingual Embedding Alignment

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 hybrid batch training with different ratios of monolingual to cross-lingual samples affect the alignment of multilingual embeddings, as measured by cross-lingual similarity metrics such as CLSim and cosine similarity?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.0/10.